Browsing by Author "Hsu, Heng-Wei"

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CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS
(2018-01-01)
Hsu, Heng-Wei
;
Wu, Tung-Yu
;
Wong, Wing Hung
;
Lee, Chen-Yi
;
電子工程學系及電子研究所
Finding the locations and identities of faces in videos is a very important task in numerous applications. In this paper, we propose a correlation-based face detection approach to improve the performance of face recognition tasks for videos. We apply correlation measures to pairs of response maps which are generated from automatically selected neurons in deep convolutional neural network (CNN) models to detect faces in each video frame. The embeddings extracted from faces cropped by our proposed approach are more consistent across each video sequence and more suitable for face recognition and clustering tasks. Experimental results from the YouTube Faces (YTF) dataset demonstrate that our proposed approach is more robust and achieves better recognition accuracy compared to state-of-the-art face detection approaches.
QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss
(2019-04-01)
Hsu, Heng-Wei
;
Wu, Tung-Yu
;
Wan, Sheng
;
Wong, Wing Hung
;
Lee, Chen-Yi
Head pose estimation has attracted immense research interest recently, as its inherent information significantly improves the performance of face-related applications such as face alignment and face recognition. In this paper, we conduct an in-depth study of head pose estimation and present a multiregression loss function, an L2 regression loss combined with an ordinal regression loss, to train a convolutional neural network (CNN) that is dedicated to estimating head poses from RGB images without depth information. The ordinal regression loss is utilized to address the nonstationary property observed as the facial features change with respect to different head pose angles and learn robust features. The L2 regression loss leverages these features to provide precise angle predictions for input images. To avoid the ambiguity problem in the commonly used Euler angle representation, we further formulate the head pose estimation problem in quaternions. Our quaternion-based multiregression loss method achieves state-of-the-art performance on the AFLW2000, AFLW test set, and AFW datasets and is closing the gap with methods that utilize depth information on the BIWI dataset.