Facial Identification Based on Transform Domains for Images and Videos
Carlos M. Travieso, Marcos DelPozo‐Baños, Jesús B. Alonso · InTech eBooks · 2011
For the last decade, researchers of many fields have pursued the creation of systems capable of human abilities. One of the most admired humans' qualities is the vision sense, something that looks so easy to us, but it has not been fully understood jet. In the scientific literature, face recognition has been extensively studied and; in some cases, successfully simulated. According to the Biometric International Group, nowadays Biometrics represent not only a main security application, but an expanding business according to Fig. 1a. Besides, as it can be seen in Fig. 1b, facial identification has been pointed out as one of the most important biometric modalities (Biometric International Group 2010). However, face recognition is not an easy task. Systems can be trained to recognize subjects in a given case. But along the time, characteristics of the scenario (light, face perspective, quality) can change and mislead the system. In fact, the own subject's face varies along the time (glasses, hats, stubble). These are major problems with which face recognition systems have to deal using different techniques. Since a face can appear in whatever position within a picture, the first step is to place it. However, this is far from the end of the problem, since within that location, a face can present a number of orientations. An approach to solve these problems is to normalize space position; variation of translation, and rotation degree; variation of rotation, by analyzing specific face reference points (Liun & He, 2008). There are plenty of publications about gender classification, combining different techniques and models trying to increase the state of the art performance. For example, (Chennamma et al., 2010) presented the problem of face or person identification from heavily altered facial images and manipulated faces generated by face transformation software tools available online. They proposed SIFT features for efficient face identification. Their dataset consisted on 100 face images downloaded from http://www.thesmokinggun.com/mugshots, reaching an identification rate up to 92 %. In (Chen-Chung & Shiuan-You Chin, 2010), the RGB images are transformed into the YIQ domain. As a first step, (Chen-Chung & ShiuanYou Chin, 2010) took the Y component and applied wavelet transformation. Then, the binary two dimensional principal components (B2DPC) were extracted. Finally, SVM was used as classifier. On a database of 24 subjects, with 6 samples per user, (Chen-Chung & Shiuan-You Chin, 2010) achieved an average identification rate between 96.37% and 100%.