Face Recognition Based on Local Image Descriptor

• Main Authors: Huang-Ming Chang, 張晃銘
• Other Authors: Ming Ouhyoung
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/65151076503981956728

碩士 === 臺灣大學 === 資訊工程學研究所 === 98 === Face recognition is an important topic in computer vision in the past decades. The rec-ognition rate of frontal faces now is higher than 99% if lighting and facial expressions are controlled. However, if the lighting, facial expression, and pose are various, or the face is under partial occlusion, the recognition rate becomes much lower. In this paper, following Hua and Akbarzadeh 2009’s approach, we implement face representation us-ing local image descriptor, and compare two faces by partial matching. We try kinds of local image descriptors to find the best one. To improve our performance, we parallelize some parts of our computation, and implement it in a quad-core system. In our first da-taset with 309 faces of 5 subjects, we get a recognition precision of 99.25% with 100 clusters; and in our second dataset with 838 faces of 8 subjects, we get a recognition precision of 99.82% with 253 clusters. These results are similar to that of Google Picasa PC version, however, ours is currently at least 8 times slower. Further speedup is ex-pected in the future work.