An Experimental Study of different Features for Face Recognition M.Hanmandlu R.Bhupesh Gupta Farrukh Sayeed A.Q.Ansari E.E Department, E.E.Department E &C Department E.E Department I.I.T. Delhi I.I.T.Delhi P.A.College of Engineering JMI, New Delhi New Delhi, India New Delhi, India Mangalore mhmandlu@gmail.com rbhupeshgupta@gmail.com sayeed.farrukh@gmail.com aqansari62@gmail.com Abstract – As a first study, the use the Gabor filter bank is made to generate features for face recognition. The features so obtained on the application of SVM classifier yields accuracy rate of 96.2%. With a view to improve the performance, two more feature types, viz., wavelet features and wavelet-fuzzy features resulting from the application of 2D wavelet transform on the Composite detail images and the Approximate images at 3 levels of decomposition, are devised. The ROCs of three feature types show that wavelet- fuzzy features have a better performance. The performance of Gabor features is slightly inferior to that of wavelet-fuzzy features. The algorithm was tested on ORL (Olivetti Research Laboratory) database that has slight orientations in face images. Keywords: Gabor filter bank, Haar wavelets, Muti- decomposition, wavelet-fuzzy features, SVM I. Introduction The face recognition is one of the most important applications of the Biometric based authentication systems. During the past few decades, it has received a considerable attention, because of a large number of application areas such as Entertainment, Smart Cards, Information Security, Law Enforcement, Surveillance etc. Face recognition involves a range of activities from many aspects of human life. Humans are good at face recognition and now machine learning is being improved to do this task. Identification of people in different environments requires three steps: acquisition, normalization and recognition. Acquisition involves the detection and tracking of face-like patches in the dynamic scenes. Normalization is the segmentation, alignment and standardization of the face images, and finally recognition is concerned with the representation via modeling of face images and association of face images with the models learned. The methods reported in the literature are broadly classified into: i) Holistic matching methods in which the whole face acts as an input to the recognition system, ii) Feature based matching methods, in which as the name indicates, the local features such as eyes, mouth, and nose are first extracted and their locations and the local statistics are fed to the recognition system, and iii) Hybrid methods where the recognition system makes use of both the local features as well as the whole face region to recognize the face. Schneiderman and Kanade [8] apply statistical likelihood tests using feature output histograms to create the detection scheme. Rowley and Kanade in [7] use neural network-based filters to obtain the benchmark results. In another early work, Papageorgiou et al. [5] propose a general object detection scheme that uses a wavelet representation and statistical learning techniques. Osuna et al. [4] apply Vapnik's support vector machine for the face detection, and Romdhani et al. [6] improve upon this work by creating the reduced training vector sets. Fleuret and Geman attempt a coarse-to-fine approach to face detection, focusing on minimizing the computation involved [3]. In perhaps the most impressive paper, Viola and Jones use the concept of an “integral image", along with the rectangular feature representation and Adaboost learning algorithm to detect faces at the rate of 15 frames per second [2]. Several basic face recognition techniques exist suitable to the frontal faces. These include: eigenfaces, neural networks, dynamic link architecture, hidden Markov model and geometrical feature matching. These approaches are analyzed in terms of the facial representations adopted by them. II. The Proposed Features Here we will be exploring three types of features which comprise Gabor, Wavelet and Wavelet-fuzzy. Our main innovation is in the second and the third types. We will now elaborate on these three feature types. A. Gabor Features Here we use the Gabor filter bank with different orientations and frequencies. The output of each filter is convolved with the original image I(x,y) and the convolved outputs of the different filters are averaged to 2011 International Conference on Communication Systems and Network Technologies 978-0-7695-4437-3/11 $26.00 © 2011 IEEE DOI 10.1109/CSNT.2011.121 567