Abstract – Over the last five years or so, deep learning networks have produced impressive results for several computer vision and pattern recognition problems. In this talk, I will discuss recent developments in the design and application of deep learning networks for problems such as face detection, pose estimation, face verification/recognition, extraction of facial attributes and subject-specific clustering. We will present a case study on unconstrained face verification and recognition highlighting the rapid progress that has been made in less than three years as well as discuss many remaining research challenges to be addressed.
Bio – Dr. Rama Chellappa is a University Distinguished Professor and a Minta Martin Professor of Engineering and Chair of the ECE department at the University of Maryland. Prof. Chellappa received the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society and four IBM Faculty Development Awards. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. Recently, he was awarded the Inaugural Leadership Award by the IEEE Biometrics Council. At UMD, he received college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. In 2010, he was recognized as an Outstanding ECE by Purdue University. In 2016, he was recognized as a Distinguished Alumni of Indian Institute of Science. Dr. Chellappa served as the Editor-inChief of PAMI. He is a Golden Core Member of the IEEE Computer Society, served as a Distinguished Lecturer of the IEEE Signal Processing Society and as the President of IEEE Biometrics Council. He is a Fellow of IEEE, IAPR, OSA, AAAS, ACM and AAAI and holds six patents.
Sponsored by the Laboratory for Analytic Sciences, The Department of Electrical and Computer Engineering, and the Army Research Office.
LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.
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