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This document outlines a student project to compare the SIFT and SURF feature extraction methods for face recognition. The project will use benchmark face datasets and extract descriptors from training and testing images using SIFT and SURF. Images will be converted to signatures using k-means clustering and an unknown face will be classified using nearest neighbors. Recognition rates will be calculated and the project will be implemented in MATLAB over several months, concluding with a written report.
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Susmit Mohan Joshi is seeking an internship or full-time position as a software engineer. He has a MS in Computer Engineering from RIT with a GPA of 3.33/4 and a bachelor's degree from the University of Mumbai with a GES GPA of 3.8/4. His skills include C++, Java, Matlab, OpenCV, and experience with projects involving facial expression recognition, emotion intensity recognition, and object recognition using techniques like PCA, SVM, and HOG.
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Susmit Mohan Joshi is seeking an internship or full-time position as a software engineer. He has a MS in Computer Engineering from RIT with a GPA of 3.33/4 and a bachelor's degree from the University of Mumbai with a GES GPA of 3.8/4. His skills include C++, Java, Matlab, OpenCV, and experience with projects involving facial expression recognition, emotion intensity recognition, and object recognition using techniques like PCA, SVM, and HOG.
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This document proposes a methodology for recognizing facial expressions with increased speed and accuracy. It involves detecting faces, preprocessing images through greyscaling and contrast adjustment, extracting features through local binary patterns, and classifying expressions. The proposed system includes face detection using Haar classifiers, image processing steps, feature detection by analyzing distances between action points on facial characteristics, and using a classifier to output the expression. Future work involves testing this approach against other methods using specialized datasets and hardware to develop a faster and more reliable facial expression recognition system.
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Proposal presentation
- 1. M.Manivasakan 2008/SP/16 Department of computer science University of Jaffna Supervisor Dr.A.Ramanan
- 2. Face recognition has been an active research topic for several decades. Owing to the wide applications in biometrics, Human Computer Interface (HCI), and many other vision related fields, many algorithms have been proposed for providing different solutions. Face recognition focuses on still images. Face recognition is commonly used in applications such as human-machine interfaces, automatic access control systems, which involve comparing an image with a database of stored faces in order to identify the subject in the image.
- 3. We intend to compare two different patch-based descriptors: SIFT and SURF. The objectives of this project are listed as follows: To study and understand feature extraction process using faces. To design a model that will recognise faces. To compare the classification performance when using different patch-based descriptors in recognising faces. To be familiar in implementing programs using MATLAB. To be able to understand concepts of classifiers to recognise different subjects using faces.
- 4. The proposed framework will be developed by a step- by-step process as mentioned below: 1) Benchmark datasets for faces will be used in the evaluation process of the system. We will consider the AT&T (formerly known as ORL) faces and Yale faces. 2) SIFT or SURF descriptors will be extracted from training and testing sets. 3) Each training and testing image will be converted into image signatures by using a clustering technique such as K-means method. The image signatures are the feature vectors for classification. 4) An unknown subject will be classified in to one of the known subject using a nearest neighbour approach. 5) The recognition rate of the system will be computed as: rate= (#correctly classified faces)/(#test faces)
- 5. March April May June July August September Reading research papers Implementation Testing and refinement Report writing
- 6. I. Lowe, D. “Distinctive image features from scale-invariant key points” International Journal of Computer Vision, 60, 2, 2004, pp. 91-110. II. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. “Speeded-up robust features (SURF)”, Computer Vision and Image Understanding, 110, 3, 2008, 346-359. III.Samaria, F.S., Harter, A.C. “Parameterisation of a Stochastic Model for Human Face Identification”, Applications of Computer Vision, 1994, 138 – 142. IV.Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting”, PAMI, 15, 27, May 2005, 0162-8828.