Face Recognition: An Engineering Approach

In computer vision, face recognition is the process of labeling a face as recognized or unrecognized. The process is based on a pipeline that goes through collection, detection, pre-processing, and recognition stages. The focus of this study is on the last stage of the pipeline with the assumption that images have already been collected and pre-processed. Conventional solutions to face recognition use the entire facial image as the input to their algorithms. We present a different approach where the input to the recognition algorithm is the individual segment of the face such as the left eye, the right eye, the nose, and the mouth. Two separate experiments are conducted on the AT&T database of faces [1]. In the first experiment, the entire image is used to run the Eigen-face, the Fisher-face, and the local binary pattern algorithms. For each run, accuracy and error rate of the results are tabulated and analyzed. In the second experiment, extracted facial feature segments are used as the input to the same algorithms. The output from each algorithm is subsequently labeled and placed in the appropriate feature class. Our analysis shows how the granularity of collected data for each segmented class can be leveraged to obtain an improved accuracy rate over the full face approach

Face recognition using statistical adapted local binary patterns.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images