Le An, (University of California, Riverside) “Real-World Person Identification”(2014)

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Le An, (University of California, Riverside) “Real-World Person Identification”(2014)

Le An, (University of California, Riverside) “Real-World Person Identification”(2014) Advisor: Bir Bhanu

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 in a non-intrusive manner. 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, the authors 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, the authors 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, the authors have proposed two methods to identify person in multiple surveillance cameras, one of which is based on a novel feature representation called reference descriptor and the other is based on sparse representation. Extensive experiments on publicly available datasets have shown that each of the aforementioned method achieves state-of-the-art performance in various person identification tasks.

For details, please check the author’s homepage.

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