UC Riverside

Person Identification or recognition has been receiving broad interests and it is highly desirable in applications such as security monitoring, authentication, etc. In order to recognize a person, different traits, including fingerprint, face, and gait, can be used. Among these possible traits, face and body are preferred since they can be acquired without the person's cooperation. In controlled environment, recognition is less challenging with well posed subject in high resolution. However, in real-world scenarios, where the image of a person exhibits variations in pose, illumination, and resolution, standard pattern recognition methods may fail. Driven by the necessity for person identification in real-world, we have proposed several identification methods. Specifically, we have developed a face image super-resolution method as a pre-processing step to improve the face recognition accuracy. In addition, to recognize person in a surveillance setting with multiple cameras, we have developed an algorithm that utilizes multiple cameras for face recognition by encoding the person-specific dynamics with a dynamic Bayesian network. In case the face of a person cannot be reliably acquired, identifying person by body appearance is preferred. To this end, we have proposed two methods to identify person in multiple surveillance cameras, using a novel reference descriptor and a sparse representation, respectively. To validate the proposed method in this dissertation, we have conducted extensive results on publicly available datasets. Results show that each of the aforementioned method achieves state-of-the-art performance in various person identification tasks.