This document presents a hybrid approach for face detection and feature extraction. It combines the Viola-Jones face detection framework with a neural network classifier to first classify images as containing a face or not. If a face is detected, Viola-Jones algorithms like integral images and cascading classifiers are used to detect the face features. Edge-based feature maps and feature vectors are also extracted and used as inputs to the neural network classifier and for future facial feature extraction. The proposed approach aims to leverage the strengths of Viola-Jones and neural networks to accurately detect faces and then extract facial features from images.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses face detection and recognition methods. It covers several topics:
1) Face detection can be categorized into knowledge-based (using facial features, skin color, templates) and image-based methods (AdaBoost, Eigenfaces, neural networks, support vector machines).
2) Important factors for face detection include facial features, skin color modeling, and template matching.
3) Face recognition uses linear/nonlinear projection methods, neural networks for 2D recognition, and 3D geometric measures for 3D recognition.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
Region based elimination of noise pixels towards optimized classifier models ...IJERA Editor
The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses face detection and recognition methods. It covers several topics:
1) Face detection can be categorized into knowledge-based (using facial features, skin color, templates) and image-based methods (AdaBoost, Eigenfaces, neural networks, support vector machines).
2) Important factors for face detection include facial features, skin color modeling, and template matching.
3) Face recognition uses linear/nonlinear projection methods, neural networks for 2D recognition, and 3D geometric measures for 3D recognition.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
Region based elimination of noise pixels towards optimized classifier models ...IJERA Editor
The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
IRJET- A Review on Fake Biometry DetectionIRJET Journal
This document summarizes a review on detecting fake biometrics. It discusses how face recognition technology has advanced but is vulnerable to spoof attacks using fake faces. The paper presents a novel software-based method for detecting fraudulent access attempts across multiple biometric systems like iris and face recognition. Experimental results on public datasets show the proposed method performs competitively compared to other state-of-the-art approaches. It analyzes the general image quality of real biometric samples to distinguish them from fake traits efficiently.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
The document discusses face tracking in videos using the Kalman filter. It first segments faces from backgrounds using histogram equalization and skin color detection. It then uses the Kalman filter to track faces under various conditions by predicting face positions based on past values, accounting for noise and motion. The Kalman filter helps reduce errors and enables real-time visual tracking. In the future, the authors plan to implement full face detection after obtaining face regions from the preprocessing and feature extraction steps discussed.
An Accurate Facial Component Detection Using Gabor FilterjournalBEEI
Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components
This document summarizes research on the effect of face tampering on face recognition. It discusses four categories of face recognition algorithms (PCA, iSVM, LBP, SIFT) and their accuracy rates under different tampering protocols using a novel tampered face image database. The conclusion is that it is unpredictable which algorithm will perform best for tampered face recognition. Motion analysis, texture analysis, and liveness detection techniques in prior research aimed to detect spoofing but may not distinguish tampered from real faces.
IRJET - Biometric Traits and Applications of FingerprintIRJET Journal
This document summarizes research on using biometric traits, particularly fingerprints, for gender identification. It first discusses how fingerprints and other biometric traits can be used for identification and verification. It then reviews several research papers on using fingerprints and other traits like gait and facial recognition for gender classification. The document finds that fingerprints provide 86% accurate results for gender identification due to their permanence and uniqueness. It lists the applications of biometrics in various fields like personal, commercial, government, and forensic use. Finally, it concludes that a proposed algorithm combining fingerprint-based techniques achieves an average 86% accuracy for gender determination.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
This document describes an iris recognition system implemented using National Instruments LabVIEW for secure voting. The system has four main stages: 1) image acquisition using an infrared camera, 2) iris localization by detecting circles in the iris image, 3) pattern matching to extract an iris code, and 4) authentication by matching the iris code to a database. The database stores voter information and iris codes in an encrypted format. On voting day, the system matches the voter's ID and captured iris image to the database to verify their identity before allowing them to vote. The system aims to provide more secure identity verification than traditional password or ID systems.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
Local Descriptor based Face Recognition SystemIRJET Journal
This document describes a local descriptor-based face recognition system that uses the Asymmetric Region Local Binary Pattern (AR-LBP) operator along with Principal Component Analysis (PCA) for facial expression recognition. The proposed AR-LBP operator addresses limitations of existing LBP operators in terms of scale, feature histogram length, and discriminability. The system divides input face images into regions, extracts AR-LBP histograms from each region, and concatenates them into a feature vector. It was evaluated on three datasets and achieved recognition accuracies of 96.43%, 97.14%, and 86.67%, respectively. Evaluation using different similarity metrics found that Mahalanobis Cosine distance performed best. Experiments varied grid and operator sizes.
Face Recognition Using Simplified Fuzzy Artmapsipij
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
This document discusses the implementation of instantaneous reactive power theory (IRP theory) for current harmonic reduction and reactive power compensation in three phase four wire power systems. IRP theory is used to calculate reference compensating currents that are injected by a shunt active power filter (SAPF) to compensate for non-linear and reactive loads. Clarke's transformation is used to convert voltages and currents to the α-β-0 reference frame for IRP calculations. Hysteresis current control with a constant switching frequency is used to generate PWM signals to control the SAPF inverter. Simulations show the SAPF reducing current harmonics and reactive power, improving the power factor by making the source current sinusoidal and in phase with
This document analyzes the NACA 6412 airfoil for use as a propeller on a flying bike. It summarizes the analysis conducted using JavaProp software. The analysis found that a two-blade propeller with the NACA 6412 airfoil would produce sufficient thrust for the flying bike while keeping weight low. Under shrouded and squared tip conditions, thrust was maximized. The analysis validated thrust values against prior mathematical modeling, meeting targets with less than 0.02% difference. It was concluded that a two-blade propeller would be better than three blades for this application due to better weight efficiency while producing equivalent thrust.
1. The document analyzes factors that determine the maximum noise produced by a train horn, including Doppler effect and sound dampening with distance.
2. Key calculations are presented to determine the critical angle at which noise is maximum, as well as the change in sound frequency and sound pressure level due to Doppler effect and distance.
3. Based on the calculations, a noise barrier height of 17 meters is recommended to mitigate train horn noise exceeding permissible levels for a train traveling at 56 km/hr, with the barrier oriented at the calculated critical angle.
This document discusses the design and analysis of an automotive front bumper beam for low-speed impact. It aims to study the key parameters of bumper beams, such as material, shape, and impact conditions to improve crashworthiness. A simulation of the bumper beam undergoing a low-velocity impact test as specified in international standards is performed. The strength of the bumper beam in elastic mode is analyzed by evaluating energy absorption and impact force at maximum deflection. Simulations are run with bumper beams made of different materials to compare deflection, impact force, stress distribution, and energy absorption. The results show that an M220 material minimizes deflection, impact force, stress, and maximizes energy absorption compared to
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
IRJET- A Review on Fake Biometry DetectionIRJET Journal
This document summarizes a review on detecting fake biometrics. It discusses how face recognition technology has advanced but is vulnerable to spoof attacks using fake faces. The paper presents a novel software-based method for detecting fraudulent access attempts across multiple biometric systems like iris and face recognition. Experimental results on public datasets show the proposed method performs competitively compared to other state-of-the-art approaches. It analyzes the general image quality of real biometric samples to distinguish them from fake traits efficiently.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
The document discusses face tracking in videos using the Kalman filter. It first segments faces from backgrounds using histogram equalization and skin color detection. It then uses the Kalman filter to track faces under various conditions by predicting face positions based on past values, accounting for noise and motion. The Kalman filter helps reduce errors and enables real-time visual tracking. In the future, the authors plan to implement full face detection after obtaining face regions from the preprocessing and feature extraction steps discussed.
An Accurate Facial Component Detection Using Gabor FilterjournalBEEI
Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components
This document summarizes research on the effect of face tampering on face recognition. It discusses four categories of face recognition algorithms (PCA, iSVM, LBP, SIFT) and their accuracy rates under different tampering protocols using a novel tampered face image database. The conclusion is that it is unpredictable which algorithm will perform best for tampered face recognition. Motion analysis, texture analysis, and liveness detection techniques in prior research aimed to detect spoofing but may not distinguish tampered from real faces.
IRJET - Biometric Traits and Applications of FingerprintIRJET Journal
This document summarizes research on using biometric traits, particularly fingerprints, for gender identification. It first discusses how fingerprints and other biometric traits can be used for identification and verification. It then reviews several research papers on using fingerprints and other traits like gait and facial recognition for gender classification. The document finds that fingerprints provide 86% accurate results for gender identification due to their permanence and uniqueness. It lists the applications of biometrics in various fields like personal, commercial, government, and forensic use. Finally, it concludes that a proposed algorithm combining fingerprint-based techniques achieves an average 86% accuracy for gender determination.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
This document describes an iris recognition system implemented using National Instruments LabVIEW for secure voting. The system has four main stages: 1) image acquisition using an infrared camera, 2) iris localization by detecting circles in the iris image, 3) pattern matching to extract an iris code, and 4) authentication by matching the iris code to a database. The database stores voter information and iris codes in an encrypted format. On voting day, the system matches the voter's ID and captured iris image to the database to verify their identity before allowing them to vote. The system aims to provide more secure identity verification than traditional password or ID systems.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
Local Descriptor based Face Recognition SystemIRJET Journal
This document describes a local descriptor-based face recognition system that uses the Asymmetric Region Local Binary Pattern (AR-LBP) operator along with Principal Component Analysis (PCA) for facial expression recognition. The proposed AR-LBP operator addresses limitations of existing LBP operators in terms of scale, feature histogram length, and discriminability. The system divides input face images into regions, extracts AR-LBP histograms from each region, and concatenates them into a feature vector. It was evaluated on three datasets and achieved recognition accuracies of 96.43%, 97.14%, and 86.67%, respectively. Evaluation using different similarity metrics found that Mahalanobis Cosine distance performed best. Experiments varied grid and operator sizes.
Face Recognition Using Simplified Fuzzy Artmapsipij
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
This document discusses the implementation of instantaneous reactive power theory (IRP theory) for current harmonic reduction and reactive power compensation in three phase four wire power systems. IRP theory is used to calculate reference compensating currents that are injected by a shunt active power filter (SAPF) to compensate for non-linear and reactive loads. Clarke's transformation is used to convert voltages and currents to the α-β-0 reference frame for IRP calculations. Hysteresis current control with a constant switching frequency is used to generate PWM signals to control the SAPF inverter. Simulations show the SAPF reducing current harmonics and reactive power, improving the power factor by making the source current sinusoidal and in phase with
This document analyzes the NACA 6412 airfoil for use as a propeller on a flying bike. It summarizes the analysis conducted using JavaProp software. The analysis found that a two-blade propeller with the NACA 6412 airfoil would produce sufficient thrust for the flying bike while keeping weight low. Under shrouded and squared tip conditions, thrust was maximized. The analysis validated thrust values against prior mathematical modeling, meeting targets with less than 0.02% difference. It was concluded that a two-blade propeller would be better than three blades for this application due to better weight efficiency while producing equivalent thrust.
1. The document analyzes factors that determine the maximum noise produced by a train horn, including Doppler effect and sound dampening with distance.
2. Key calculations are presented to determine the critical angle at which noise is maximum, as well as the change in sound frequency and sound pressure level due to Doppler effect and distance.
3. Based on the calculations, a noise barrier height of 17 meters is recommended to mitigate train horn noise exceeding permissible levels for a train traveling at 56 km/hr, with the barrier oriented at the calculated critical angle.
This document discusses the design and analysis of an automotive front bumper beam for low-speed impact. It aims to study the key parameters of bumper beams, such as material, shape, and impact conditions to improve crashworthiness. A simulation of the bumper beam undergoing a low-velocity impact test as specified in international standards is performed. The strength of the bumper beam in elastic mode is analyzed by evaluating energy absorption and impact force at maximum deflection. Simulations are run with bumper beams made of different materials to compare deflection, impact force, stress distribution, and energy absorption. The results show that an M220 material minimizes deflection, impact force, stress, and maximizes energy absorption compared to
The document presents a study on a novel polynomial speed hump and its effects on car dynamics using a quarter-car model. A polynomial hump profile is generated in MATLAB that starts and ends with zero displacement, velocity, and acceleration. The dynamics of a quarter-car model crossing polynomial humps of varying heights, lengths, and speeds are investigated. The sprung mass displacement and maximum acceleration are examined to evaluate ride comfort. Simulation results show the effect of hump dimensions and speed on sprung mass response. The proposed polynomial hump aims to provide smoother kinematics for ride comfort compared to other hump designs.
This document discusses applying cell zooming to compensate for cell outages in mobile networks. Cell zooming involves adjusting transmission power levels or antenna heights to vary cell sizes. The document develops an algorithm using the Okumura-Hata propagation model to calculate the transmission power or antenna height needed for outage compensation. Simulation results using a planning software show that cell zooming can achieve outage compensation with minimal human intervention, in line with self-organizing network (SON) objectives.
This document summarizes a research paper that proposes two methods called EFFORT and ANYCAST to maximize the lifetime of wireless sensor networks. EFFORT is an opportunistic routing protocol that improves path diversity and transmission reliability. ANYCAST is an asynchronous sleep-wake scheduling mechanism that arranges sensor nodes to sleep to reduce energy consumption. The document describes how EFFORT selects forwarder nodes based on an opportunistic energy cost metric (OECS) that considers several energy costs. It also explains how ANYCAST works by having sensor nodes wake independently during probing periods to relay data, then return to sleep if no response is received. Simulation results showed both EFFORT and ANYCAST effectively extend network lifetime compared to other routing
This document presents an optimization study of friction stir welding process parameters for aluminum alloys to achieve maximum tensile strength. Experiments were conducted using Taguchi's design of experiments method to evaluate the effects of rotational speed, tool tilt angle, and travel speed on joint strength. Analysis of variance revealed that tool tilt angle was the most influential parameter, contributing 47.39% to tensile strength, followed by travel speed at 42.18%. The optimal parameters predicted were 1300 rpm rotational speed, 1° tool tilt angle, and 60 mm/min travel speed, which were expected to yield a maximum tensile strength of 285 MPa. Experimental validation of these optimal parameters produced a joint strength of 288 MPa, close to the predicted value.
The document proposes a behavioral model called PFMDA to detect anomalous packet dropping and modification attacks in wireless ad hoc networks. The PFMDA scheme establishes a routing tree with the sink node at the root. As data packets are transmitted along the tree, each sender or forwarder adds a small number of "packet marks" to the packet. This allows the sink to determine the dropping ratio for each node and identify nodes that are definitely dropping/modifying packets or are suspicious of such behavior. The scheme uses node categorization and heuristic ranking algorithms to gradually identify misbehaving nodes with few false positives. The goal is to detect packet droppers and modifiers within the network.
This document discusses the need for higher education institutions to establish a technology infrastructure to support mobile governance (m-governance). It begins by defining m-governance as using mobile devices to improve government services and access information anytime, anywhere. The document then argues that m-governance can complement existing e-governance efforts by providing another channel for stakeholders in higher education to access important information. It presents the benefits of m-governance for higher education, such as improved efficiency, reduced costs and barriers, and equal access to information regardless of location. Finally, the document proposes a broad framework for a university's m-governance technology infrastructure to facilitate interaction between the institution and its stakeholders through mobile devices
This document compares the use of a PI controller and fuzzy logic controller for speed control of an induction motor. It first provides background on induction motors and common speed control techniques. It then describes the basic structure and advantages of PI controllers and fuzzy logic controllers for motor speed control. Specifically, it details the design of a fuzzy logic controller for this application, including the 49 rule base and membership functions used. Finally, it presents the MATLAB/Simulink models used to simulate and compare the PI controller and fuzzy logic controller for induction motor speed control.
This document provides a review of approaches for enhancing the performance of vehicular ad hoc networks (VANETs) using CSMA-MACA. It begins with an introduction to VANETs and discusses some of their key properties including high dynamic topology, frequent disconnections, and the hidden terminal problem. The document then reviews related work on hybrid approaches combining CSMA and MACA for routing in VANETs. Finally, it provides details on commonly used routing protocols like AODV and discusses how accounting for obstacles in simulations can improve safety performance evaluations of VANET applications.
This document describes a proposed interactive voice response (IVR) and dual-tone multi-frequency (DTMF) based voting system. The system would allow voters to cast their votes via phone by pressing numbers on their keypad in response to IVR prompts, without needing to physically go to a polling place. It would use two databases - one to store voter information and another to store information about candidates/parties. Voters would call into the system using their assigned voter ID and password. The system would track votes cast and prevent double voting. After voting closed, administrators could generate results reports displayed as graphs. The proposed system aims to make voting more convenient and accessible compared to traditional in-person methods.
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes a study on solid waste generation from the construction of office buildings for Nepal Telecom in Nepal. The study examined waste produced at three construction sites in Kathmandu, Kaski, and Rupandehi districts from 2010 to 2013. It found that major waste streams included concrete, reinforcement bars, wood, and bricks. Construction methods were outdated and waste was poorly managed. Factors contributing to waste included lack of planning, training, monitoring, and government rules. While theoretical understanding of waste management existed, practical implementation was lacking. Principal wastes could be reused or recycled, though potential for reduction was not fully addressed. This study provides a baseline for policies to improve construction waste management in Nepal.
This document describes the design of a three-layer knowledgebase for expert systems. The three layers are:
1) The logical/design layer, which processes input text to extract clauses, functional symbols, and predicate symbols.
2) The knowledge layer, which represents the extracted knowledge using techniques like first-order logic, frames, and description logic.
3) The storage layer, which stores the represented knowledge in an object-relational database for retrieval and use by the expert system.
The document provides details on each layer and discusses how the input text is processed in the logical layer before being represented and stored in the knowledge and storage layers, respectively.
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses hiding confidential text data within an encrypted image using reversible data hiding techniques. It begins by introducing the concepts of reversible data hiding and meaningful image encryption. The proposed method first encrypts an original image using AES to create a pre-encrypted image. It then applies discrete wavelet transform to the pre-encrypted image to transform it into a visually meaningful encrypted image. Finally, reversible data hiding is used to hide confidential text data within the meaningful encrypted image while still allowing lossless retrieval of both the image and hidden data. The method aims to provide effective data protection with low computational cost.
This document discusses using a Distributed Power Flow Controller (DPFC) to improve the voltage profile of a transmission system and mitigate power quality issues like voltage sags and swells. It first introduces the concepts of voltage sags, swells, and power quality in transmission systems. It then proposes using a DPFC, which consists of STATCOM shunt controllers and multiple SSSC series controllers, to regulate voltage. Two control methods for the DPFC are analyzed: 1) PI control and 2) Fuzzy Logic control. Simulation results in MATLAB/Simulink show that the DPFC is able to improve the voltage profile at the load bus compared to without using a DPFC. Fuzzy Logic control is proposed to further enhance
This document describes a CMOS RF bandpass filter design using an active inductor with a compensated negative resistance circuit. It begins by introducing the need for integrated RF filters and issues with passive inductors. It then describes an active inductor topology based on the gyrator principle and a common drain-common gate negative resistance circuit to compensate losses. Simulation results show the filter achieves a center frequency tuning range of 1.9-6 GHz and quality factors above 60 when tuned. The compensated active inductor approach allows for an integrated tunable bandpass filter solution for multi-standard wireless applications.
Abstract: This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The main study of face detection is detect the portion of part and mention the circle or rectangular of the every portion of body. In this paper face detection is depend upon the face pattern which is match the face from the pattern reorganization. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on viola jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm.Keywords: Face detection, Video frames, Viola-Jones, Skin detection, Skin color classification, Face reorganization, Pattern reorganization. Skin Color.
Title: Face Detection Using Modified Viola Jones Algorithm
Author: Alpika Gupta, Dr. Rajdev Tiwari
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Paper Publications
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
This document summarizes a research paper on face recognition using principal component analysis (PCA). It discusses how PCA can be used to reduce the dimensionality of face images for recognition. The system detects faces in images, extracts features using PCA, and then compares new faces to those in a training database to recognize identities. The results showed an accuracy of 87.09% on a test set of 30 images using this PCA-based approach for face recognition. While effective, the system has limitations when faces vary significantly from the training data. Overall, PCA provides a way to analyze face patterns and identify faces with reasonable accuracy under controlled conditions.
1) The document presents a new face parts detection algorithm that combines the Viola-Jones object detection framework with geometric information of facial features.
2) It detects faces, then isolates regions of interest for the eyes, nose, and mouth. Eye pupils are located using iris recognition techniques.
3) The algorithm was tested on hundreds of images and showed promising results for automated facial feature detection.
IRJET- A Review on Various Techniques for Face DetectionIRJET Journal
This document provides a review of various techniques for face detection. It summarizes several approaches including Viola Jones, genetic algorithms, convolutional neural networks, support vector machines, Hough transforms with convolutional neural networks, MMX feature extraction, and Minmax with embedding. For each approach, it discusses the methodology, advantages, and disadvantages. It finds that while each approach has benefits for face detection, they also have limitations and no single approach is clearly superior in all cases and environments. The goal of the study is to systematically compare the different techniques to better understand how each contributes to success in face detection.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
This document provides a comprehensive review of techniques for face detection and recognition systems. It begins with an abstract that outlines face detection and recognition technology and its use in identification and verification. The introduction discusses the challenges of automatic face recognition compared to human face recognition abilities. Section II reviews recent face detection techniques, including feature-based and image-based approaches. Section III discusses unsupervised classification-based approaches for face recognition, including Eigenfaces, dynamic graph matching, and geometrical feature matching. Section IV addresses intelligent supervised approaches like neural networks and support vector machines. The conclusion compares different face databases and provides an overall assessment of current face recognition research.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
The document discusses a proposed system for facial expression recognition using Gabor filters and linear basis functions. It involves detecting faces, extracting features using Gabor filters, classifying expressions using a linear model trained on Gabor phase shifts, and evaluating performance. The proposed system aims to address issues with existing representation learning methods and achieve state-of-the-art expression recognition results on both posed and spontaneous expressions.
Progression in Large Age-Gap Face VerificationIRJET Journal
1. The document discusses progression in large age-gap face verification. It summarizes various techniques used for face recognition from conventional methods to deep neural networks.
2. A typical face recognition system includes image acquisition, face detection, normalization, feature extraction, and then matching. The document reviews several feature extraction methods like Eigenfaces, Fisherfaces, and local binary patterns.
3. Recent deep learning approaches like convolutional neural networks have improved face recognition accuracy. One study used CNNs with metric learning and achieved high accuracy on the Labeled Faces in the Wild dataset. Encoding image microstructures and identity factor analysis has also been effective for matching similar faces.
This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN).
The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCVIRJET Journal
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
IRJET - Facial Recognition based Attendance System with LBPHIRJET Journal
This document presents a facial recognition based attendance system using LBPH (Local Binary Pattern Histograms). It begins with an abstract describing the system which takes student attendance using facial identification from classroom camera images. It then discusses related work in attendance and face recognition systems. The proposed system workflow is described involving face detection, feature extraction using LBPH, template matching, and attendance recording. Experimental results demonstrate the system's ability to detect multiple faces and record attendance accurately in an Excel sheet with date/time. The conclusion discusses how the system reduces human effort for attendance and increases learning time compared to traditional methods.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
IRJET - Emotionalizer : Face Emotion Detection SystemIRJET Journal
This document describes a facial emotion detection system called Emotionalizer. The system uses machine learning to analyze facial expressions in images and detect emotions like happy, sad, angry, fearful and disgust. It was developed in Python using techniques like pre-processing, skin color detection, facial feature extraction and a support vector machine classifier. The goal is to build a system that can automatically recognize emotions from faces as accurately as humans. It discusses previous related work on facial recognition and detection and outlines the objectives, methodology and evaluation of the Emotionalizer system.
IRJET- Emotionalizer : Face Emotion Detection SystemIRJET Journal
This document describes a face emotion detection system called Emotionalizer. It uses machine learning and facial recognition techniques to detect emotions like happy, sad, angry, fearful and disgust based on facial expressions. The system analyzes images of faces and determines the appropriate emotion based on geometric changes in facial features. It was developed in Python using tools like OpenCV for facial detection and recognition. The goal is to build a system that can read emotions from facial expressions similarly to how humans perceive emotions.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
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K017247882
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. IV (Mar – Apr. 2015), PP 78-82
www.iosrjournals.org
DOI: 10.9790/0661-17247882 www.iosrjournals.org 78 | Page
A Hybrid Approach to Face Detection And Feature Extraction
Ranjana Sikarwar1
, Arun Agrawal2
, and Shivpratap Singh Kushwah3
Research Scholar, Deptt Of Computer Science & Engg, ITM-GOI, Gwalior (M.P.) INDIA1
Asst. Prof., Deptt. Of Computer Science & Engg., ITM-GOI, Gwalior (M.P.) INDIA2
Asst. Prof., Deptt. Of Computer Science & Engg., ITM-GOI, Gwalior (M.P.) INDIA3
Abstract: This paper presents a hybrid approach to face classification and detection. The remarkable
advancement in technology has enhanced the use of more accurate and precise methods to identify and
recognize things. Face detection and identification is a new field because face is undeniably related to its owner
except in case of identical twins. This paper presents a combination of three well known algorithms Viola- Jones
face detection framework, Neural Networks method to detect faces in static images. Face recognition is a kind
of biometric detection using the physiological measure to identify and detect face. It allows the user to passively
identify the person and its features using biometric. The proposed work emphasizes on the face detection and
identification using Viola-Jones algorithm which is a real time face detection system. Neural Networks will be
used as a classifier between faces and non-faces.
Keywords: Face detection and identification; Viola-Jones algorithm; feature vectors; Integral images;
Adaboost.
I. Introduction
Face detection has been considered as the most complicated and challenging dilemma in the field of
computer visualization, due to the many intra-class changes caused by the variations in facial look, illumination,
and expression. These changes make the face distribution process to be extremely complex and nonlinear in all
space dimensions which is linear to the given image space dimension. Moreover, in day to day life the
implementation of biometric and other real life methods make the device constrain to create variations which
make the distribution of human faces in feature space more widespread and complicated in space dimension
than that of frontal faces. Consequently, the problem related to robust face detection becomes more complex.
Terrific advancement in the field of face detection methods has been made from decades and it has
emerged as a new area of study for new researchers these days. Numerous face detection techniques emphasis
only on detecting frontal faces with good luminance conditions.
This paper deals with the study of the methods used in first two steps of the proposed algorithm, i.e.
classification between face and non-face and then face detection. Many methods can be used for face detection
like neural network, Adaboost algorithm and other methods proposed by Viola- Jones [7]. Various methods
proposed by Viola- Jones for face detection are described in section II in the further sections the paper.
The main purpose of the face detection is to identify human faces in images. Some possible
applications for automatic face detection are:
A. supervision and security applications,
B. video-conference applications,
C. animation of facial expressions,
D. remote camera control applications,
The existing methods face the problems described below:
a) Too high computational (time-, space-) complexity and/or
b) Too low effectiveness.
Presently it is necessary to stress on the fact that automatic face detection as well as most other
automatic object-detection methods is a very pompous task, especially because of significant sample variations,
which can’t be easily analytically described with parameters. The face detector performs very well using a Viola
Jones based method. It is not too fast, but gives accurate result.
II. Literature review
The problem of face detection refers to determining whether or not there are any faces in a given image
[01]. There have been various approaches proposed for face detection, which could be generally classified into
four categories [02]: template matching based methods, feature-based methods, knowledge-based methods, and
learning based methods. Template matching based method means the final decision comes from the similarity
2. A Hybrid Approach to Face Detection And Feature Extraction
DOI: 10.9790/0661-17247882 www.iosrjournals.org 79 | Page
measurement between input image and the template. It is scale dependent, rotation-dependent and computational
expensive. Feature-based methods use low-level features such as intensity [3], color [4], edge, shape[5], and
texture to locate facial features, and find out the face location. Knowledge based methods [6] detected an
isosceles triangle (for frontal view) or a right triangle (for side view). Learning based methods use various
training samples to make the classifier to be capable of judging face from non-face.
According to survey of Yang, the face detection technique can be categorized into four types and are
explained below.
Knowledge-based methods:- In this method the rules are coded by humans to represent the facial feature
distinctly for e.g. Symmetry between eyes, position of nose and mouth underneath the nose.
1) Feature invariant methods:- In this method those features which are difficult to pose, condition of
lightening or rotation are considered. Colors of skin, edges and shapes fall into their class.
2) Template matching methods:- In this method the correlation between a given test image and pre-selected
facial templates is calculated.
3) Appearance-based methods:- In this approach an advanced machine learning method is used to extract the
discriminative features from a pre-labeled training set to find out facial features.
III. Related work
Recently, a lot of research is being done in the vision community to accurate face detector in real work
application, in particular, the seminal work by viola and Jones [7]. The Viola and Jones face detector has
become the defacto standard to built successful face detection in real time, however, it produces a high false
positive (detecting a face when there is none) and false negative rate (not detecting a face that’s present) when
directly applied to the input image. To handle this problem, various improvements have been proposed, such as
using skin color filters (whether pre- filtering or post-filtering) to provide complementary information in color
images. Though many experimental results have demonstrated the feasibility of combining SCF with VJFD to
reduce false positive, both methods suffer from high false negative rate as some face regions may be ignored by
detector, when directly applied to the input image with complexes background[08][09][17]. This can
significantly decrease the accuracy of any face application domains, attributed to the fact that, when a face
region is missed, the next stages of the system cannot retrieve the missed face. Therefore, false negative
determine the eventual success or failure of the subsequent stages. The methods and frameworks related to the
proposed work are discussed below.
Identifiction Of One Or More Person
Fig 1: Machine face recognition algorithm
A. Viola–Jones object detection framework
The first object detection framework to provide competitive object detection rates in real-time was
given by Paul Viola and Michael Jones in the year 2001. Although it can be taught to find out a variety of
object classes, it was inspired chiefly by the crisis of face detection. This algorithm as cvHaarDetectObjects().
In Viola-Jones system a simple characteristic is used, with relation to the attribute sets. Viola and Jones
make note that the choice of features instead of a statistical pixel based system is important due to the benefit of
3. A Hybrid Approach to Face Detection And Feature Extraction
DOI: 10.9790/0661-17247882 www.iosrjournals.org 80 | Page
ad-hoc domain encoding. It is particularly important in case of face detection. Facial appearance can be used to
signify both the statistically close facial information and sparsely related background data in a sample image.
Viola-Jones detector is a better, binary classifier build of several weak detectors. Each weak detector is
particularly simple binary classifier.
During the learning stage, many weak detectors are trained to achieve the desired hit rate / miss rate
using Adaboost .To detect objects, the original image is segmented into smaller rectangular subparts, each of
which is submitted to the cascade as shown in fig 2.
If a rectangular image patch passes through all of the cascade stages, then it is classified as “positive” The
process is repeated at different scales.
The basic, weak classifier is based on a very simple visual feature (those kind of features are often
referred to as “Haar like features”.
Paul Viola and Michael Jones opened an approach for object detection which minimizes computation
time while achieving high detection accuracy earlier.
The first contribution is related to computing a dense set of image features with the aid of integral
image. To obtain ideal scale invariance, mostly all object detection systems must operate on multiple image
scales. In integral image, by removing the requirement to compute a multi-scale image pyramid reduces the
initial image processing required for object detection significantly.
The second contribution is pertaining to feature selection based on AdaBoost which is forceful and
efficient technique for feature selection.
The third contribution is a strategy for synthezing a cascade of classifiers which thoroughly reduce
computation complexity while enhancing detection accuracy. In the beginning stages the cascade is planned to
decline a majority of the image to focus later execution on prominent regions.
Fig 3: Edge based processing
B. Edge-Based Feature Maps
Edge-based feature maps are the very bases of our image representation algorithm. The feature maps
represent the distribution of four-direction edges extracted from a 64×64- pixel input image. The input image is
first subjected to pixel by-pixel spatial filtering operations using kernels of 5 × 5- pixel size to detect edges in
four directions, i.e. horizontal, +45 degree, vertical, or -45 degree. The threshold for edge detection is
determined taking the local variance of luminance data into account. Namely, the median of the 40 values of
neighboring pixel intensity differences in a 5 × 5- pixel kernel is adopted as the threshold. This is quite
important to retain all essential features in an input image in the feature maps. The edge information is very well
extracted from both bright and dark images.
C. Feature Vectors
64-dimension feature vectors are generated from feature maps by taking the spatial distribution
histograms of edge flags. In this work, three types of feature vectors, two general-purpose vectors and a face-
4. A Hybrid Approach to Face Detection And Feature Extraction
DOI: 10.9790/0661-17247882 www.iosrjournals.org 81 | Page
specific vector generated from the same set of feature maps were employed to perform multiple-clue face
detection algorithm [1].
Fig. 3 illustrates the feature vector generation procedure in the projected principal-edge distribution
(PPED) [9]. This provides a general purpose vector. In the horizontal edge map, for example, edge flags in
every four rows are accumulated and the spatial distribution of edge flags along the vertical axis is represented
by a histogram.
D. Integral Image
Accuracy and speed are the two important parameters for a good face detection algorithm .But there is
always a conflict between these two. So a new image representation known as integral image is used.
Integral images are easily understandable and are built by just taking the sum of the luminance values
above and to the left of a pixel in the given image. Viola and Jones exploit the fact that the integral image is
obtained by double integrating the sample image; it is done initially along the rows then preceded by columns.
An integral image is used to increase the speed that is used in feature extraction because any rectangle
in an image can be found from those integral images, in only four indexes to the integral image.
IV. Proposed work
In the proposed work we combine two common approaches, one based on neural network for
classification between face and non-face and other based on face detection using Viola and Jones algorithm.
The selection of the type of neural network as a classifier between face and non-face and the selection
of Viola-Jones methods for face detection is yet to be worked upon on and will be published in the final
implementation paper as a part of this study work.
The basic methodology of the algorithm is as follows:
Firstly the neural network needs to be trained for a defined set of test images of faces. The types of
neural network selected for training can be a self-organizing map (SOM) using an unsupervised learning
technique can be used to classify to identify if the subject in the input image is “present” or “not present” in the
image database, multilayer feed forward network, Multi-Layer Feed-Forward Backpropagation Network . One
of these neural networks can be used in our proposed work for classification between face and non-face using
one of the training algorithms like Scale Gradient Conjugate(SCG), traincgf(Fletcher-Powell conjugate gradient
back-propagation) in any version of MATLAB using neural network toolbox.
After the training of the neural network the input image can be passed into it and if the outcome is a
face then it can be passed through the next step of processing for face detection.
Viola-Jones face detection framework can be used for detection of the human face. Any one of the
three methods of viola-Jones i.e. Integral image, Ada-boost and Cascade classifier can be used in our proposed
work [7].
For example the basic operation of a cascade classifier can be operated as follows:
Find an image in all regions, which contain possible candidates for an eye, then on the basis of geometric face
characteristics try to join two candidates into an eye pair and finally, confirm or refuse the face candidate using
complexion information.
The method was projected over a set of quite different images, i.e. the training set.
The Goal of method was to reach maximum classification accuracy on the images, which meet the following
demands and constraints, respectively (beside already mentioned two):
• Plain background,
• Uniform ambient illumination,
• Fair-complexion faces, which must be present in the image in their entirety (frontal position) and
• Faces move round for at most 30 degrees.
The method’s effectiveness was tested over an independent image set, i.e. the testing set.
The basic principle of operation is shown on Fig. 2.
The method requires some thresholds, which plays a crucial role for proper processing. It is set quite loosely
(tolerantly), but it becomes effective as a sequence. All thresholds were defined using the training set.
After the detection of face in our proposed work this proposed work can be extended to facial feature detection
and extraction.
Table below shows the summary of facial feature extraction approaches carried out by different authors.
5. A Hybrid Approach to Face Detection And Feature Extraction
DOI: 10.9790/0661-17247882 www.iosrjournals.org 82 | Page
Table1. Summary Of Facial Feature Extraction Techniques
Author Approach No. Of
Feature
Video/Still-
Frontal/
Rotated
T. Kanade,
1997
Geometry
Based
eyes, mouth
and
nose
still-frontal
A. Yuille, D.
Cohen, and P.
Hallinan, 1989
Template
Based
eyes,
Mouth,
nose
and
eyebrow
still-frontal
C. Chang, T.S.
Huang, and C.
Novak, 1994
Skin color based Eyes and/or
mouth
still and video
frontal
initially in a near
frontal position
and
therefore both
eyes
are visible
V. Conclusion And Future Work
In this study work we have proposed an approach for face detection using neural networks and Viola-
Jones face detection method. After the implementation of this combined approach the work can be further
extended for feature localization and extraction. This approach will be implemented initially on static frontal
images. Histogram equalization can also be applied for adjusting the contrasts in images in future work
accompanied by noise removal.
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