Jenn-Jier James Lien

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Current approaches to automated analysis have focused on a small set of prototypic expressions (e.g., joy or anger). Prototypic expressions occur infrequently in everyday life, however, and emotion expression is far more varied. To capture the full range of emotion expression, automated discrimination of fine-grained changes in facial expression is needed.(More)
Automated recognition of facial expression is an important addition to computer vision research because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI). We developed a computer vision system that automatically recognizes individual action units or action unit combinations in the upper face(More)
We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is performed using Hidden Markov Models (HMMs). Three methods are(More)
Human action recognition is an important issue in the pattern recognition field, with applications ranging from remote surveillance to the indexing of commercial video content. However, human actions are characterized by non-linear dynamics and are therefore not easily learned and recognized. Accordingly, this study proposes a silhouette-based human action(More)
We developed a novel learning-based human detection system, which can detect people having different sizes and orientations, under a wide variety of backgrounds or even with crowds. To overcome the affects of geometric and rotational variations, the system automatically assigns the dominant orientations of each block-based feature encoding by using the(More)
Automatically locating multiple feature points (i.e., the shape) in a facial image and then synthesizing the corresponding facial sketch are highly challenging since facial images typically exhibit a wide range of poses, expressions, and scales, and have differing degrees of illumination and/or occlusion. When the facial sketches are to be synthesized in(More)
A learning-based super-resolution system consisting of training and synthesis processes is presented. In the proposed system, a multi-resolution wavelet approach is applied to carry out the robust synthesis of both the global geometric structure and the local high-frequency detailed features of a facial image. In the training process, the input image is(More)