Face Recognition using Multilevel Block Truncation Coding

Face Recognition is one of the fastest growing biometric technologies to be used in real time applications as it requires lesser user co-operation when compared to other biometrics like fingerprint and iris recognition. Such applications require a very less recognition time and allow for a little leeway on the accuracy front; this is achieved by finding out the feature vector of a face image. The paper presents use of Multilevel Block Truncation coding for face recognition. In all four levels of Multilevel Block Truncation Coding are considered for feature vector extraction resulting into four variations of proposed face recognition technique. The experimentation has been conducted on two different face databases. The first one is Face Database which has 1000 face images and the second one is “Our Own Database” which has 1600 face images. To measure the performance of the algorithm the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) parameters have been used. The experimental results have shown that the outcome of BTC Level 4 is better as compared to the other BTC levels in terms of accuracy, at the cost of increased feature vector size.

[1]  Sudeep D. Thepade,et al.  Image retrieval using augmented block truncation coding techniques , 2009, ICAC3 '09.

[2]  J Shermina,et al.  Illumination invariant face recognition using Discrete Cosine Transform and Principal Component Analysis , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[3]  Sudeep D. Thepade,et al.  Iris recognition using texture features extracted from Walshlet pyramid , 2011, ICWET.

[4]  Josef Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jie Ma,et al.  A novel face recognition method based on Principal Component Analysis and Kernel Partial Least Squares , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[6]  Pong C. Yuen,et al.  Incremental Linear Discriminant Analysis for Face Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Zhao Lihong,et al.  Face Recognition Method Based on Adaptively Weighted Block-Two Dimensional Principal Component Analysis , 2011, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks.

[8]  Wang Ye,et al.  Face recognition based on independent component analysis , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[9]  Sudeep D. Thepade,et al.  Augmentation of Block Truncation Coding based Image Retrieval by using Even and Odd Images with Sundry Colour Spaces , 2010 .

[10]  Sudeep D. Thepade,et al.  Improved CBIR using Multileveled Block Truncation Coding , 2010 .

[11]  Lidija Mandić,et al.  COLOUR APPEARANCE MODELS , 2002 .

[12]  K. Baskaran,et al.  Recognition of Faces Using Improved Principal Component Analysis , 2010, 2010 Second International Conference on Machine Learning and Computing.

[13]  S. Annadurai,et al.  Implementation of incremental linear discriminant analysis using singular value decomposition for face recognition , 2009, 2009 First International Conference on Advanced Computing.

[14]  Sudeep D. Thepade,et al.  Boosting Block Truncation Coding with Kekre ’ s LUV Color Space for Image Retrieval , 2022 .

[15]  Yunxia Li,et al.  Face Recognition by Nonnegative Independent Component Analysis , 2009, 2009 Fifth International Conference on Natural Computation.