Face Identification Based on Singular Value Decomposition and Data Fusion
Yun Wang · Chinese Journal of Computers · 2000
A face identification method based on singular value decomposition (SVD) and data fusion is proposed in this paper. The singular values (SVs) and singular value vectors of a face image matrix are extracted and employed as features. These features are matched using SVs and singular value vectors respectively. The matching outputs are based on SVs and reconstruction errors and expressed by membership function values. For more accurate results, these membership function values are fused by LOGISTIC regression. The proposed identification technique improves the correct verification rates from the following reasons. First, fusion makes accurate identification results. Second, the method solves the problem of small sample size that is difficult to avoid in face recognition problem. Third, the LOGISTIC regression fusion method has the learning ability. So it can learn both “positive” and “negative” samples and the correct identification rates are achieved. The ORL face database is used in experiment and experiment results show that the novel face verification method is effective and possesses several desirable properties when it compared with many existing methods.