Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 32, No. 1 (January 2019) 78-84    Article in Press

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  FACE RECOGNITION IN THERMAL IMAGES BASED ON SPARSE CLASSIFIER
 
M. Shavandi and I. E. P. Afrakoti
 
( Received: November 16, 2018 – Accepted in Revised Form: January 03, 2019 )
 
 

Abstract    Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent years, using thermal images has gain more attention among the introduced solutions as an effective and unique solution. This article studies the performance of sparse processing techniques when facing with challenges of face recognition problem in thermal images. Also, the potential of the sparse classifier algorithm to receive information directly from input images without using any feature extraction algorithms was studied. The obtained results indicated that the sparse processing techniques outperform the Eigenface and KNN algorithms in terms of addressing the challenges of thermal images. In this work, USTC NVIN and CBSR NIR face datasets were used for simulation purposes. These datasets include the images with different emotional states (sad, happy, etc.) captured in different light conditions; also the images are captured both with and without wearing glasses. Simulation results have shown that sparse classifier can be an effective algorithm for the face recognition problem in thermal images.

 

Keywords    Face Recognition, Sparse Representations Classification, Thermal Images, Norm l^0

 

چکیده   

سیستم‌های شناسایی چهره علی¬رغم پیشرفت‌های بسیاری که داشته‌اند به دلیل طیف وسیع تغییرات چهره انسان (تغییراتی مانند: نور، عینک، چرخش سر، حالت‌های عاطفی مختلف) هنوز هم با مشکلاتی مواجه هستند. راه حل‌های مختلفی از سوی محققان جهت غلبه بر این مشکلات مطرح شده است، اما در سال‌های اخیر از بین این راه حل‌ها استفاده از تصاویر حرارتی به عنوان راه حلی موثر و خاص مورد توجه قرار گرفته است. در این مقاله به بررسی عملکرد روش‌های پردازش تنک در مواجه با چالش‌های شناسایی تصاویر حرارتی چهره پرداخته شده است. همچنین توانمندی الگوریتم طبقه‌بند تنک در دریافت اطلاعات به صورت مستقیم از تصاویر ورودی بدون استفاده از هیچ گونه الگوریتم استخراج ویژگی مورد ارزیابی قرار گرفته است. نتایج بدست آمده از شبیه‌سازی حاکی از برتری روش‌های پردازش تنک نسبت به الگوریتم‌های Eigenface، KNN در مقابله با چالش‌های تصاویر حرارتی می‌باشد. در این کار از دو مجموعه داده‌ی USTC.NVIN وCBSR NIR Face Dataset جهت شبیه‌سازی استفاده شده است. تصاویر موجود در این پایگاه‌های داده شامل تصاویر با حالت چهره مختلف (غم،شادی و...)، که در شرایط نوری متفاوتی تهیه شده‌اند و همچنین تصاویر باعینک و بی‌عینک می‌باشد. نتایج شبیه‌سازی توانایی الگوریتم‌های تنک را در مواجهه با مساله شناسایی چهره در تصاویر حرارتی تایید می‌کند.

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