Face recognition and detection
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This document provides an overview of face recognition techniques. It discusses biometric identification using physiological and behavioral characteristics like fingerprints, iris, voice, etc. It describes the two types of face recognition comparisons: identification and verification. Feature extraction and holistic methods for face recognition are presented. Popular machine learning algorithms for classification tasks like KNN, LWL, Naive Bayes, decision trees are summarized. Finally, common applications and references are listed.
Face recognition and detection
- 1. GUIDE : Mrs NEHA GUPTA GGMrsguiEHA GUPTA Team members GUIDE : Mrs NEHA GUPTAGUIDE : Mrs NEHA GUPTA Team members Team members
- 2. A biometric is a physiological or behavioral characteristic of a human being that can distinguish one person from another and that theoretically can be used for identification or verification of identity.” Biometric applications available today are categorized into 2 sectors - Psychological: Iris, Fingerprints, Hand, Retinal and Face recognition - Behavioral: Voice, Typing pattern, Signature
- 3. Facial recognition is a form of computer vision that uses faces to attempt to identify a person or verify a person’s claimed identity. For face recognition there are two types of comparisons, cont… 1) IDENTFICATION - figure out “Who is X?” - accomplished by system performing a “one-to-many ” search 2) VERIFICATION - answer the question “Is this X?” - accomplished by the system performing a “one-to-one” search.
- 4. Face Recognition Face recognition systems (FRSs) are an important field in computer vision, because it represent a non-invasive BI technique. 1. A face detection algorithm is used for extracting faces from video frames (training videos) and generating a face database. 2. Filtering and preprocessing are applied to face images obtained in the previous step. 3. A collection of machine learning algorithms are trained using as input data the faces obtained in the previous step. 4. Finally, the classifiers are used for classify faces obtained from video frames
- 5. Describe the different methods of face recognition- Feature extraction methods- The input image to identify and extract (and measure) distinctive facial features such as the eyes, mouth, nose, etc. Compute the geometric relationships among those facial points, thus reducing the input facial image to a vector of geometric features. Holistic methods- Holistic approaches attempt to identify faces using global representations, i.e., descriptions based on the entire image rather than on local features of the face.
- 6. During the past decades, several ML algorithms have been proposed for classification tasks. Most of them are from the theoretical view under some assumption about data distribution, characteristics of the classification task, signal to-noise-ratio, etc. In reality, these assumptions are often hard to be verified. Therefore, a practical solution for selecting an appropriate model for a given classification task is to experimentally compare these algorithms. Five widely used machine classifiers- K-Nearest Neighbor (KNN) Locally-Weighted Learning (LWL) Naive Bayes classifier (NB) Decision Table Classifier (DT) Single Decision Tree (SDT).
- 7. 1.Replacement of PIN 2. Border control 3. Voter verification 4. Computer security 5. Government Use 6. Security/Counterterrorism. 7. Immigration 8. Commercial Use 9. Residential Security 10. Banking using ATM
- 8. 1.E. Garc´ ıa Amaro, M.A. Nu ˜ no-Maganda and M. Morales-Sandoval, “Evaluation of Machine Learning Techniques for Face Detection and Recognition”, IEEE 2012. 2. Claudia Iancu, Peter Corcoran and Gabriel Costache,” A Review of Face Recognition Techniques for In-Camera Applications”, IEEE 2007. 3. Brian C. Becker, Enrique G.Ortiz, “Evaluation of Face Recognition Techniques for Application to Facebook ” 2008 IEEE 4. D. Bhattacharyya, R. Ranjan, F. Alisherov, and M. Choi, “Biometric authentication: A review,” International Journal of u- and e- Service, Science and Technology, vol. 3, no. 2, pp. 23– 27, 2009. 5. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006 6. G. Bradski and A. Kaehler, Learning OpenCV. O’Reilly Media Inc., 2008.