IJE TRANSACTIONS A: Basics Vol. 31, No. 10 (October 2018) 1698-1707   

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B. Sabzalian and V. Abolghasemi
( Received: March 04, 2018 – Accepted in Revised Form: August 17, 2018 )

Abstract    Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost function is proposed in order to incorporate sparsity which is controlled by a specific parameter and weights of feature coefficients. This method extracts highly localized patterns, which generally improves the capability of face recognition. After extracting patterns by IWNS-NMF, we use principle component analysis to reduce dimension for classification by linear SVM. The Recognition rates on ORL, YALE and JAFFE datasets were 97.5, 93.33 and 87.8%, respectively. Comparisons to the related methods in the literature indicate that the proposed IWNS-NMF method achieves higher face recognition performance than NMF, NS-NMF, Local NMF and SNMF.


Keywords    Non-negative matrix factorization, face recognition, pattern analysis, features extraction, sparse representation


چکیده    تجزیه ماتریس‌های نامنفی (NMF) یک روش ارائه تصویر مبتنی بر بخش‌بندی است. این روش از ایده‌ای نشأت می‌گیرد که تصویر یک چهره را از ترکیب چند بخش می‌سازد. در این مقاله به ارائه روشی برای تشخیص چهره با استفاده از یافتن نمایه‌های محلی بخش‌بندی شده به نام "تجزیه ماتریس‌های نامنفی با اوزان تکرارشونده (IWNS-NMF)" می‌پردازیم. در این روش یک تابع هزینه پیشنهاد می‌شود که با استفاده از یک پارامتر و اوزان ضرایب ویژگی‌ها میزان تنک‌سازی را کنترل می‌کند .این روش با استخراج الگوهای با اهمیت محلی قابلیت و دقت تشخیص چهره را بهبود می‌بخشد. بعد از مرحله استخراج الگوها توسط روش ارائه‌شده، به‌منظور کاهش ابعاد ویژگی‌ها از روش آنالیز مؤلفه‌های اصلی (PCA) بهره برده و در ادامه از روش SVM خطی برای طبقه‌بندی استفاده کرده‌ایم. نرخ شناسایی چهره در پایگاه‌های داده ORL، YALE و JAFFE به ترتیب 97/5%، 93/33% و 87/8% است. با مقایسه روش‌های مشابه پیشین می‌توان به این نتیجه رسید که روش ارائه‌شده IWNS-NMF در تشخیص چهره از کارایی و دقت بیشتری نسبت به روش‌هایی نظیرNMF ، NS-NMF، Local-NMF و SNMF برخوردار است.


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