An improved face recognition system based on soft biometric modals - Xie, Collin Ding Chang (2010)
Biometric systems which recognize individuals by facial features or characteristics have become increasingly popular in web applications like Facebook or Twitter. This allows auto-tagging of photographs uploaded into individual’s user account. The most basic block of any biometric recognition system would be the presence of a human trait which exhibits distinctiveness, permanence, universality, collectability, acceptability and resistance to circumvention. Automatic face recognition scans an individual’s face and matches it against a library of known faces. The need to improve the performance of face recognition arises due to its inability to provide perfect recognition by possessing all the properties of a human trait (facial) used in human recognition. This final year project proposes the use of soft biometric traits to complement the identity information obtained from primary biometric identifier like face. Despite the fact that characteristics like skin tone, hair colour, shirt colour and gender are not as reliable and unique, they are still able to provide some information about the user. This report shows some soft biometric traits can be automatically extracted through the use of skin segmentation technique and modifications of the Viola-Jones Face Detection Algorithm. The results from these implementations and gender classification by adaptive boosting of Gabor features are integrated in the decision making process for the primary face recognition system. Weighted majority voting is applied and weighting the decisions of those more qualified experts heavier may further improve the overall performance of the face recognition system. The system uses scores to express the similarity between a pattern and a biometric template. In other words, the higher the fusion score, the higher the similarity is between them. A single threshold which separates the two groups of scores is utilized to identify between the genuine user and the imposters. By using more than one means of biometric identification, the multimodal biometric system is able to retain its high threshold recognition setting. In a comparative study done to substantiate the motivation of this project, it is found out that among the soft biometric traits chosen to enhance the primary face recognition system, improvement is most significant when differentiating siblings of opposite sex. The recognition results achieved an accuracy rate of approximately 80% as compared to 20% without soft biometrics. Such multimodal biometric system based on different biometric identifiers is proven to be more robust to non-universality and noise.