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Title:
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COMPARING THE PERFORMANCE OF RECOVERY ALGORITHMS FOR ROBUST FACE RECOGNITION |
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Author(s):
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Sedigheh Ghofrani, Seyedramin Alikiaamiri, Mehran Khorasani |
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ISBN:
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978-989-8533-38-8 |
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Editors:
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Katherine Blashki and Yingcai Xiao |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Face Recognition, Sparse Representation, Recovery Algorithm. |
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Type:
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Full Paper |
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First Page:
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219 |
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Last Page:
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226 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The problem of automatically recognizing human faces from the facial images under varying expression and illumination is still attractive. The sparse signal representation is a new topic for object classification and face recognition. Implementing the sparse representation needs using a proper reconstruction algorithm to recover the sparse signal as well. In this paper, the performance of three greedy algorithms named matching pursuit (MP), orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) and two 1?-minimization algorithms named primal-dual interior-point (PDIP) and Homotopy are compared according to achieved accuracy, consuming time, and the number of iterations. For this purpose, three well known datasets named, AR, Extended Yale B and Essex University are used. |
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