State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Human face recognition under degraded conditions

Comparative studies on the state of the art feature extraction and classification techniques for human face recognition under low resolution problem, are proposed in this work. Also, the effect of applying resolution enhancement, using interpolation techniques, is evaluated. A gradient-based illumination insensitive preprocessing technique is proposed using the ratio between the gradient magnitude and the current intensity level of image which is insensitive against severe level of lighting effect. Also, a combination of multi-scale Weber analysis and enhanced DD-DT-CWT is demonstrated to have a noticeable stability versus illumination variation. Moreover, utilization of the illumination insensitive image descriptors on the preprocessed image leads to further robustness against lighting effect. The proposed block-based face analysis decreases the effect of occlusion by devoting different weights to the image subblocks, according to their discrimination power, in the score or decision level fusion. In addition, a hierarchical structure of global and block-based techniques is proposed to improve the recognition accuracy when different image degraded conditions occur. Complementary performance of global and local techniques leads to considerable improvement in the face recognition accuracy. Effectiveness of the proposed algorithms are evaluated on Extended Yale B, AR, CMU Multi-PIE, LFW, FERET and FRGC databases with large number of images under different degradation conditions. The experimental results show an improved performance under poor illumination, facial expression and, occluded images

Illumination tolerance in facial recognition

In this research work, five different preprocessing techniques were experimented with two different classifiers to find the best match for preprocessor + classifier combination to built an illumination tolerant face recognition system. Hence, a face recognition system is proposed based on illumination normalization techniques and linear subspace model using two distance metrics on three challenging, yet interesting databases. The databases are CAS PEAL database, the Extended Yale B database, and the AT&T database. The research takes the form of experimentation and analysis in which five illumination normalization techniques were compared and analyzed using two different distance metrics. The performances and execution times of the various techniques were recorded and measured for accuracy and efficiency. The illumination normalization techniques were Gamma Intensity Correction (GIC), discrete Cosine Transform (DCT), Histogram Remapping using Normal distribution (HRN), Histogram Remapping using Log-normal distribution (HRL), and Anisotropic Smoothing technique (AS). The linear subspace models utilized were principal component analysis (PCA) and Linear Discriminant Analysis (LDA). The two distance metrics were Euclidean and Cosine distance. The result showed that for databases with both illumination (shadows), and lighting (over-exposure) variations like the CAS PEAL database the Histogram remapping technique with normal distribution produced excellent result when the cosine distance is used as the classifier. The result indicated 65% recognition rate in 15.8 ms/img. Alternatively for databases consisting of pure illumination variation, like the extended Yale B database, the Gamma Intensity Correction (GIC) merged with the Euclidean distance metric gave the most accurate result with 95.4% recognition accuracy in 1ms/img. It was further gathered from the set of experiments that the cosine distance produces more accurate result compared to the Euclidean distance metric. However the Euclidean distance is faster than the cosine distance in all the experiments conducted

An Accurate Facial Component Detection Using Gabor Filter

Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial component

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Study of Different Algorithms for Face Recognition

The importance of utilising biometrics to establish personal authenticity and to detect impostors is growing in the present scenario of global security concern. Development of a biometric system for personal identification, which fulfils the requirements for access control of secured areas and other applications like identity validation for social welfare, crime detection, ATM access, computer security, etc., is felt to be the need of the day [2]. Face recognition has been evolving as a convenient biometric mode for human authentication for more than last two decades. It plays an important role in applications such as video surveillance, human computer interface, and face image database management [1]. A lot of techniques have been applied for different applications. Robustness and reliability becomes more and more important for these applications especially in security systems. Basically Face Recognition is the process through which a person is identified by his facial image. With the help of this technique it is possible to use the facial image of a person to authenticate him into any secure system. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods. Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. This work studies the different approaches for a Face Recognition System. The different approaches like PCA, DCT and different types of Wavelets have been studied with the help of Euclidean distance as a classifier and Neural Network as a classifier. The results have been compared for the two database, AMP which contains 975 images of 13 individuals (each person has 75 different images) under various facial expressions and lightning condition with each image being cropped and resized to 64×64 pixels for the simulation and ORL (Olivetti Research Lab) which contains 400 images (each with 112×92 pixels) corresponding to 40 persons in 10 poses each including both male and female. The ORL database image has been resized to 128×128 pixels