The performance of the proposed method is compared with LHD method for face recognition in various conditions: 1) ideal condition of face, 2) varying lighting conditions, 3) varying poses and 4) varying face expression. It is very encouraging that the proposed method gives lower computational cost than LHD while keeping the accuracy of face recognition equal to the LHD method. | 152 Science and Technology Development Journal, vol 20, 2017 A modification of line Hausdorff distance for face recognition to reduce computational cost Dang Nguyen Chau, Do Hong Tuan Abstract— Face recognition, that has a lot of applications in modern life, is still an attractive research for pattern recognition community. Due to the similarity of human faces, face recognition presents a significant challenge for pattern recognition researchers. Hausdorff distance is an efficient parameter for measuring the similarity between objects. Line Hausdorff distance (LHD) technique, which is the applying of Hausdorff distance for face recognition, gives high accuracy in comparing with common methods for face recognition. For fast screen techniques such as LHD, the computational cost is a key issue. A modified Line Hausdorff distance (MLHD) is proposed in this paper. The performance of the proposed method is compared with LHD method for face recognition in various conditions: 1) ideal condition of face, 2) varying lighting conditions, 3) varying poses and 4) varying face expression. It is very encouraging that the proposed method gives lower computational cost than LHD while keeping the accuracy of face recognition equal to the LHD method. Index Terms— Face cognition, Line Hausdorff Distance, Hausdorff Distance, Modified Line Hausdorff Distance. 1 INTRODUCTION A utomatic face recognition is an active research area and has had a lot of publications in last two decade years. Face recognition has lot of applications in modern life such as bank card identification, access control, ‘mug shot’ searching, security monitoring systems. Face recognition is used for identification one or more persons from a still image or a video by Manuscript Received on March 15th, 2017, Manuscript Revised on November 01st, 2017. Dang Nguyen Chau, Ho Chi Minh City University of Technology – VNU-HCM, Hochiminh City, Vietnam (e-mail: chaudn@). Do Hong Tuan, Ho Chi Minh City
