Robust and fast face detection using CNN based facial component heat map and face bound regression = CNN 기반의 얼굴 요소의 히트맵과 경계 영역 회귀법을 이용한 강력한 고속의 얼굴 검출 방법에 관한 연구

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Face detection is the first step in a wide range of face applications, such as facial keypoint localization, face recognition, facial expression analysis and other topics. As a special task of object detection, a broad study of face detection has been conducted. The standard face detection dataset has several challenges for face detection. In particular, it contains faces at the wide range of scales, illumination and requires more precise localization due to occlusions and pose changes. Although recent studies show that deep learning approaches can achieve impressive performance on most computer vision tasks, face's localization caused by the above mentioned problems were not be fully resolved yet. There are additional challenges such as the repetitive convolution operation in the sliding window method and the resizing procedure due to the fixed size constraint of the fully-connected layers, which make it have a high computational complexity. Our main inspiration is to construct fully convolutional network. The architecture takes arbitrarily-sized input and produces last layer's feature maps of corresponding size represented as facial component heat map. Add to this we adopt multi-task learning technique. To address these challenges, we propose three-fold: (1) multi-scale proposal network for facial component heat map, (2) detection network with face bound regression, (3) hard sample mining to enhance the effectiveness of detection network by selecting the informative samples. We adopt the standard evaluation protocol to strictly validate our method. Experimental results present that our method outperforms other existing methods having used the standard FDDB dataset for face detection with 83.29% in the true positive rate at 1,034 false alarms. From the comparison of runtime efficiency, we can verify our method with competitive computational complexity. Our approach casts a light on face detection commonly deemed as a barrier which could not find the breakthrough in a trade-off between the performance and processing speed.
Ro, Yong Manresearcher노용만researcher
한국과학기술원 :전기및전자공학부,
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학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2016.8 ,[iv, 24 p. :]


face detection; convolutional neural network; fully convolutional network; transfer learning; split convolutional layer; heat map; multi-task; multi-scale; facial component; face bound regression; 얼굴 검출; 컨볼루셔널 신경망; fully 컨볼루셔널 신경망; 전이 학습; 분기된 컬볼루셔널 신경망; 히트맵; 멀티태스크; 멀티스케일; 얼굴의 구성 요소; 경계 영역 회귀법

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