Enhanced long-range personal identification based on multimodal information of human features

This work presents an enhanced long-range personal identification scheme using multimodal information of human features. Multimodal information includes multiview face detection, height measurement and face recognition. Multiview faces are estimated by collecting five face databases that correspond to left, half-left, right, half-right, and frontal faces, respectively. The sequences of parameters based on four detectors are also designed to determine the face direction. The detectors use the head-shoulder region, frontal face, profile face, and eyes detector respectively. In addition to determining when individuals enter the monitoring area, the multiview face detection module also describes the detected face direction. This result allows the identification system to select the face database of a specific direction to identify subsequent faces. Additionally, the height measurement module estimates individual height by calculating the vanishing points and lines. The module concept is based on single-view metrology. The measured information further refines the face database selected by multiview face detection and minimizes the candidates for face identification. Importantly, the proposed method integrates the multimodal information based on face direction, height and face features to refine the database and analyzes the information to determine the identity of a person. In this work, images from a monitoring area 5.6 m away from a camera are captured using an inexpensive digital web camera. The experimental results show that the proposed method can improve the accuracy rate by more than 21 % in contrast with the baselines and correspondingly demonstrates the effectiveness of the proposed idea.