A Novel Partitioned Gradient Fisherface Algorithm for Robust Face Recognition

• Main Authors: Li-Nien Chu, 朱立年
• Other Authors: Shang-Hong Lai
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/23393364067626157634

碩士 === 國立清華大學 === 資訊工程學系 === 92 === In this thesis, we propose a novel algorithm for face recognition under illumination variations. Our approach is mainly based on the Fisherface analysis and consists of three effective strategies to overcome the illumination variation problem. The first one is the use of the image gradient features instead of intensity features to be robust against illumination variations. The second one is image partition with adaptive block comparison. We appropriately divide the face region into several blocks for comparison and assign large weights to reliable blocks depending on the relative PCA reconstruction error. By the way, the regions of missing data will be given a smaller weight and then we can reduce the effect of missing data. The third one is the use of a novel similarity measure between face images. This measure is based on the L2 and Mahalanobis-L2 distances and combines both PCA and LDA coefficients to determine the similarity. By employing these strategies into the proposed algorithm, we can achieve more robust recognition. We show that the proposed algorithm significantly outperforms other well-known appearance-based approaches through experiments on several benchmarking face databases. The superior performance if the proposed algorithm becomes notable for severe lighting conditions that introduce strong shadows. The proposed partitioned gradient PCA algorithm is proved to be successful in the face recognition experiments when only one training image per person is available. Our experimental results show that our algorithm successfully overcomes the illumination variation problem in face recognition and it is feasible for practical applications.