Improving the performance of MPCA+MDA for Face Recognition
محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران
سال انتشار: 1390
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,014
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شناسه ملی سند علمی:
ICEE19_226
تاریخ نمایه سازی: 14 مرداد 1391
چکیده مقاله:
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis (MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis (MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, the main criterion of algorithm is similar to Sequential mode truncation (SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension manually. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on CMPU-PIE databases are provided
کلیدواژه ها:
subspace learning ، multilinear principal component analysis ، feature extraction ، Multilinear discriminant analysis ، tensor objects
نویسندگان
Seyyedeh Maryam Hosseyni nia
MSc Student at Biomedical Engineering and Medical Physics Shahid Beheshti University, Tehran, Iran
Fatemeh Roosta
MSc Student at Biomedical Engineering and Medical Physics Shahid Beheshti University, Tehran, Iran
Ali Akbar Shams Baboli
MSc Student at Department of Electrical Engineering, University of Science and Technology, Tehran, Iran
Gholamali Rezai rad
Associate professor at Department of Electrical Engineering, University of Science and Technology
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