کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947699 | 1439588 | 2017 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Locality-constrained linear coding based bi-layer model for multi-view facial expression recognition
ترجمه فارسی عنوان
مدل دو لایه مبتنی بر کدگذاری خطی محدوده محلی برای تشخیص بیان چندگانه
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کلمات کلیدی
به رسمیت شناختن صورت چند نمایه، مدل دو لایه مبتنی بر کدگذاری مبتنی بر محدوده محلی، کیف از ویژگی های،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Multi-view facial expression recognition is a challenging and active research area in computer vision. In this paper, we propose a simple yet effective method, called the locality-constrained linear coding based bi-layer (LLCBL) model, to learn discriminative representation for multi-view facial expression recognition. To address the issue of large pose variations, locality-constrained linear coding is adopted to construct an overall bag-of-features model, which is then used to extract overall features as well as estimate poses in the first layer. In the second layer, we establish one specific view-dependent model for each view, respectively. After the pose information of the facial image is known, we use the corresponding view-dependent model in the second layer to further extract features. By combining all the features in these two layers, we obtain a unified representation of the image. To evaluate the proposed approach, we conduct extensive experiments on both BU-3DFE and Multi-PIE databases. Experimental results show that our approach outperforms the state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 239, 24 May 2017, Pages 143-152
Journal: Neurocomputing - Volume 239, 24 May 2017, Pages 143-152
نویسندگان
Jianlong Wu, Zhouchen Lin, Wenming Zheng, Hongbin Zha,