کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
528418 869566 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Face detection by structural models
ترجمه فارسی عنوان
تشخیص چهره با استفاده از مدل های ساختاری
کلمات کلیدی
شناسایی چهره، مدل سازه، همبستگی صورت و بدن
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We enrich face detection model by hierarchical structure and part subtype.
• We propose to explore the face-body co-occurrence to improve face detection.
• We achieve state-of-the-art performance on FDDB, AFW and a self-annotated dataset.

Despite the successes in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild due to large appearance variations. Instead of leaving appearance variations directly to statistical learning algorithms, we propose a hierarchical part based structural model to explicitly capture them. The model enables part subtype option to handle local appearance variations such as closed and open month, and part deformation to capture the global appearance variations such as pose and expression. In detection, candidate window is fitted to the structural model to infer the part location and part subtype, and detection score is then computed based on the fitted configuration. In this way, the influence of appearance variation is reduced. Besides the face model, we exploit the co-occurrence between face and body, which helps to handle large variations, such as heavy occlusions, to further boost the face detection performance. We present a phrase based representation for body detection, and propose a structural context model to jointly encode the outputs of face detector and body detector. Benefit from the rich structural face and body information, as well as the discriminative structural learning algorithm, our method achieves state-of-the-art performance on FDDB, AFW and a self-annotated dataset, under wide comparisons with commercial and academic methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 32, Issue 10, October 2014, Pages 790–799
نویسندگان
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