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INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
INTELLIGENT FACE RECOGNITION TECHNIQUES
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INTELLIGENT FACE RECOGNITION TECHNIQUES

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Face recognition is a form of biometric identification. A biometrics is, “Automated methods of recognizing an individual based on their unique physical or behavioral characteristics.” The process of …

Face recognition is a form of biometric identification. A biometrics is, “Automated methods of recognizing an individual based on their unique physical or behavioral characteristics.” The process of facial recognition involves automated methods to determine identity, using facial features as essential elements of distinction.

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  • 1. A Presentation On “ INTELLIGENT FACE RECOGNITION TECHNIQUES ” Presented by: CHIRAG JAIN
  • 2. INTELLIGENT FACE RECOGNITION TECHNIQUES O U T L I N E  INTRODUCTION  SURVEY OF THE FACE RECOGNITION  STRENGTHS OF EFFICIENT FACE RECOGNITION METHODS  ALGORITHM FOR FACE RECOGNITION
  • 3. Introduction Face recognition is a form of biometric identification. A biometrics is, “Automated methods of recognizing an individual based on their unique physical or behavioral characteristics.” The process of facial recognition involves automated methods to determine identity, using facial features as essential elements of distinction.
  • 4. Solution To The Problem Of Machine Recognition Of Faces Face Recognition Segmentation of faces from cluttered scenes Feature extraction from the face regions Recognition or verification
  • 5. SURVEY OF THE FACE RECOGNITION o o o SEGMENTATION / DETECTION FEATURE EXTRACTION METHODS RECOGNITION FROM INTENSITY IMAGES
  • 6. SURVEY OF THE FACE RECOGNITION o o o SEGMENTATION / DETECTION FEATURE EXTRACTION METHODS RECOGNITION FROM INTENSITY IMAGES The first step in any automatic face recognition systems is the detection (segmentation) of faces in Images. Up to the mid-1990s, most work on Segmentation was focused on single-face segmentation from a simple or complex background. These approaches included using a whole face template, a deformable feature-based template, skin color, and a neural network.
  • 7. SURVEY OF THE FACE RECOGNITION o SEGMENTATION / DETECTION o FEATURE EXTRACTION METHODS RECOGNITION FROM INTENSITY IMAGES Three types of feature extraction methods can be distinguished: (1) generic methods based on edges, lines, and curves; (2) feature-template-based methods that are used to detect facial features such as eyes; (3) structural matching methods that take into consideration geometrical constraints on the features. o These methods have difficulty when the appearances of the features change significantly, for example, closed eyes, eyes with glasses, open mouth.
  • 8. SURVEY OF THE FACE RECOGNITION o SEGMENTATION / DETECTION FEATURE EXTRACTION METHODS o RECOGNITION FROM INTENSITY IMAGES o These methods are classified into three main categories: 1.holistic matching methods, 2. feature-based matching methods and 3. hybrid methods. Holistic matching methods use the whole face region as the raw input to a recognition system.Using PCA, many face recognition techniques have been developed: eigen faces, featureline based methods, Fisher faces, Bayesian methods and SVM methods. In Feature-based matching methods, local features such as the eyes, nose, and mouth are first extracted and their locations and local statistics (geometric and/or appearance) are fed into a structural classifier. Hybrid methods are based on using both local features and the whole face region to recognize a face, as the human perception system uses.
  • 9. STRENGTHS OF EFFICIENT FACE RECOGNITION METHODS The PCA method is a low dimensional procedure for the characterization of human faces based on Principal Component Analysis also known as Karhunen-Loeve (Kor eigen space, seeks the direction in the input space along which most of the image variations lies. This approach reduces the dimension size of an image greatly. As % recognition using PCA is low. We propose a system in which we first find out Discrete Wavelet Transform of a given image and then cascaded with PCA. Advantages of wavelet transformations are to preserve the structure of data, computation complexity, vanishing moments, compact support, and decorrelared coefficients. The Contour Matching method has advantages in storage space requirements.
  • 10. VARIOUS ALGORITHM FOR FACE RECOGNITION PCA METHOD DWT+PCA APPROACH CALCULATION OF DWT OF A GIVEN IMAGE CONTOUR MATCHING METHOD
  • 11. PCA METHOD Figure 1: High-level functioning principle of the Eigen face-based facial recognition algorithm
  • 12. DWT+PCA APPROACH We use term Wavelet PCA to refer to computing principle components for a masked or modified set of wavelet coefficient to find Wavelet PCA eigen spectra in spectral domain and then projecting the original image onto the wavelet PCA eigen spectra basis. In this way, features at a particular scale are indirectly emphasized by the computed projection basis enhancing the reduced dimensionality images without filtering artifacts.
  • 13. CALCULATION OF DWT OF A GIVEN IMAGE The Square Two-Dimensional DWT is calculated using a series of one dimensional DWTs which is as follows: Step 1: Replace each image row with its 1D DWT Step 2: Replace each image column with its 1D DWT Step 3: Repeat steps (1) and (2) on the lowest sub-band to create the next scale Step 4: Repeat step (3) until the desired number of scales has been created.
  • 14. CONTOUR MATCHING METHOD The proposed face recognition system is a three step process i.e. 1) Preprocessing and normalization. 2) Contour Generation. 3) Matching Algorithm.
  • 15. References    Rama Chellappa, Charles L. Wilson and Saad Sirohey, “Human and machine recognition of faces: A survey,” Proceedings of the IEEE vol.83,no.5.pp.705-740 May1995. Mehmoud Ismail, Reda A. El-Khoribi “HMT of the Ranklet Transform for Face Recognition and Verification,” ICGST’s Graphics Vision and Image Processing Journal, vol.6,issue3,dec06,pp7-13. L.Sirovich and M. Kirby, “Low dimensional procedure for the characterization of human faces,” Journal of optical society of America, vol.4, March1987,pp519- 524.
  • 16. Queries if any?? Thanks CHIRAG JAIN

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