Academic Publications

Yan Xu*, Yu Cheng*, Jian Zhao, Zhecan Wang, Lin Xiong, Jayashree Karlekar, Hajime Tamura, Tomoyuki Kagaya, Shengmei Shen, Sugiri Pranata,
Jiashi Feng, Junliang Xing
Workshop on MS-Celeb-1M Challenge with ICCV 2017, , 2017.10

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam varius felis aliquet, fermentum nisl at, molestie nisi. In hac habitasse platea dictumst. Maecenas luctus lectus eros, vitae malesuada risus cursus porta. Aliquam non enim malesuada quam lobortis lobortis. Fusce ac sapien volutpat, pulvinar ligula sed, sagittis arcu. Nam sagittis sem sit amet vulputate mollis. Donec non mattis lectus, quis tincidunt quam. Phasellus elementum pharetra tellus imperdiet elementum. Maecenas tempor efficitur quam, in mattis velit congue eu. Etiam egestas ultrices consequat. Sed a nisl tellus. Donec orci diam, lobortis ac orci et, eleifend posuere lectus. Etiam sollicitudin turpis a augue maximus tempus. Sed egestas mauris non elit congue, a varius mi elementum. Aliquam at ligula ante. Fusce imperdiet sapien ac dapibus fringilla.

Donec in arcu tortor. Vivamus eleifend lorem a fringilla malesuada. Praesent vitae mauris vehicula, vulputate dui non, consequat quam. Integer tellus diam, ultrices et iaculis eget, rhoncus in odio. Mauris vel aliquam lorem. Maecenas ac hendrerit ligula. Sed quis mi id elit faucibus viverra. Duis ac justo ac turpis blandit hendrerit. Donec lacus massa, blandit in quam non, maximus finibus diam.

Integer a felis massa. Nunc ac maximus tortor. Aliquam velit augue, tincidunt suscipit semper eget, imperdiet ut risus. Suspendisse sit amet porttitor sapien. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Quisque rhoncus tristique euismod. Fusce pharetra, augue nec auctor faucibus, diam orci auctor tortor, eu gravida magna neque non dolor.

Integer quis accumsan arcu. Pellentesque posuere faucibus risus. Fusce sed feugiat turpis. Vivamus euismod ex vitae lorem dapibus, id efficitur ligula pellentesque. Nulla facilisi. Maecenas luctus nisl at ornare venenatis. Mauris maximus nisl mauris.

Suspendisse a ultrices ipsum, at venenatis risus. Praesent ornare enim purus, ac varius leo convallis et. Nam porta, justo a hendrerit suscipit, sem enim cursus arcu, ac pellentesque eros felis a lacus. Vivamus tristique, ex sed blandit feugiat, ex nunc rutrum felis, in efficitur ante lectus vel massa. Praesent molestie finibus laoreet. Sed ultrices, eros eu cursus finibus, est libero sollicitudin ex, ac cursus lacus lectus ac purus. Donec bibendum vitae velit vitae aliquet. Fusce volutpat finibus tempor. Integer vulputate porta eleifend. Etiam mattis nibh ut aliquet consectetur. Nulla pharetra lectus eget aliquam tempus. Nulla facilisi. Nulla ultricies nisi vel urna posuere, eu porttitor erat maximus. Praesent viverra volutpat turpis, nec porttitor orci vulputate porta. Nulla fermentum aliquet placerat.

Hao Liu, Jiashi Feng, Zequn Jie, Jayashree Karlekar, Bo Zhao, Meibin Qi, Jianguo Jiang, Shuicheng Yan
International Conference on Computer Vision (ICCV), 2017.10

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam varius felis aliquet, fermentum nisl at, molestie nisi. In hac habitasse platea dictumst. Maecenas luctus lectus eros, vitae malesuada risus cursus porta. Aliquam non enim malesuada quam lobortis lobortis. Fusce ac sapien volutpat, pulvinar ligula sed, sagittis arcu. Nam sagittis sem sit amet vulputate mollis. Donec non mattis lectus, quis tincidunt quam. Phasellus elementum pharetra tellus imperdiet elementum. Maecenas tempor efficitur quam, in mattis velit congue eu. Etiam egestas ultrices consequat. Sed a nisl tellus. Donec orci diam, lobortis ac orci et, eleifend posuere lectus. Etiam sollicitudin turpis a augue maximus tempus. Sed egestas mauris non elit congue, a varius mi elementum. Aliquam at ligula ante. Fusce imperdiet sapien ac dapibus fringilla.

Donec in arcu tortor. Vivamus eleifend lorem a fringilla malesuada. Praesent vitae mauris vehicula, vulputate dui non, consequat quam. Integer tellus diam, ultrices et iaculis eget, rhoncus in odio. Mauris vel aliquam lorem. Maecenas ac hendrerit ligula. Sed quis mi id elit faucibus viverra. Duis ac justo ac turpis blandit hendrerit. Donec lacus massa, blandit in quam non, maximus finibus diam.

Integer a felis massa. Nunc ac maximus tortor. Aliquam velit augue, tincidunt suscipit semper eget, imperdiet ut risus. Suspendisse sit amet porttitor sapien. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Quisque rhoncus tristique euismod. Fusce pharetra, augue nec auctor faucibus, diam orci auctor tortor, eu gravida magna neque non dolor.

Integer quis accumsan arcu. Pellentesque posuere faucibus risus. Fusce sed feugiat turpis. Vivamus euismod ex vitae lorem dapibus, id efficitur ligula pellentesque. Nulla facilisi. Maecenas luctus nisl at ornare venenatis. Mauris maximus nisl mauris.

Suspendisse a ultrices ipsum, at venenatis risus. Praesent ornare enim purus, ac varius leo convallis et. Nam porta, justo a hendrerit suscipit, sem enim cursus arcu, ac pellentesque eros felis a lacus. Vivamus tristique, ex sed blandit feugiat, ex nunc rutrum felis, in efficitur ante lectus vel massa. Praesent molestie finibus laoreet. Sed ultrices, eros eu cursus finibus, est libero sollicitudin ex, ac cursus lacus lectus ac purus. Donec bibendum vitae velit vitae aliquet. Fusce volutpat finibus tempor. Integer vulputate porta eleifend. Etiam mattis nibh ut aliquet consectetur. Nulla pharetra lectus eget aliquam tempus. Nulla facilisi. Nulla ultricies nisi vel urna posuere, eu porttitor erat maximus. Praesent viverra volutpat turpis, nec porttitor orci vulputate porta. Nulla fermentum aliquet placerat.

Keke Liu, Yazhou Liu, Quansen Sun, Sugiri Pranata, Shengmei Shen
Pacific-Rim Conference On Multimedia (PCM), 2017.09

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam varius felis aliquet, fermentum nisl at, molestie nisi. In hac habitasse platea dictumst. Maecenas luctus lectus eros, vitae malesuada risus cursus porta. Aliquam non enim malesuada quam lobortis lobortis. Fusce ac sapien volutpat, pulvinar ligula sed, sagittis arcu. Nam sagittis sem sit amet vulputate mollis. Donec non mattis lectus, quis tincidunt quam. Phasellus elementum pharetra tellus imperdiet elementum. Maecenas tempor efficitur quam, in mattis velit congue eu. Etiam egestas ultrices consequat. Sed a nisl tellus. Donec orci diam, lobortis ac orci et, eleifend posuere lectus. Etiam sollicitudin turpis a augue maximus tempus. Sed egestas mauris non elit congue, a varius mi elementum. Aliquam at ligula ante. Fusce imperdiet sapien ac dapibus fringilla.

Donec in arcu tortor. Vivamus eleifend lorem a fringilla malesuada. Praesent vitae mauris vehicula, vulputate dui non, consequat quam. Integer tellus diam, ultrices et iaculis eget, rhoncus in odio. Mauris vel aliquam lorem. Maecenas ac hendrerit ligula. Sed quis mi id elit faucibus viverra. Duis ac justo ac turpis blandit hendrerit. Donec lacus massa, blandit in quam non, maximus finibus diam.

Integer a felis massa. Nunc ac maximus tortor. Aliquam velit augue, tincidunt suscipit semper eget, imperdiet ut risus. Suspendisse sit amet porttitor sapien. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Quisque rhoncus tristique euismod. Fusce pharetra, augue nec auctor faucibus, diam orci auctor tortor, eu gravida magna neque non dolor.

Integer quis accumsan arcu. Pellentesque posuere faucibus risus. Fusce sed feugiat turpis. Vivamus euismod ex vitae lorem dapibus, id efficitur ligula pellentesque. Nulla facilisi. Maecenas luctus nisl at ornare venenatis. Mauris maximus nisl mauris.

Suspendisse a ultrices ipsum, at venenatis risus. Praesent ornare enim purus, ac varius leo convallis et. Nam porta, justo a hendrerit suscipit, sem enim cursus arcu, ac pellentesque eros felis a lacus. Vivamus tristique, ex sed blandit feugiat, ex nunc rutrum felis, in efficitur ante lectus vel massa. Praesent molestie finibus laoreet. Sed ultrices, eros eu cursus finibus, est libero sollicitudin ex, ac cursus lacus lectus ac purus. Donec bibendum vitae velit vitae aliquet. Fusce volutpat finibus tempor. Integer vulputate porta eleifend. Etiam mattis nibh ut aliquet consectetur. Nulla pharetra lectus eget aliquam tempus. Nulla facilisi. Nulla ultricies nisi vel urna posuere, eu porttitor erat maximus. Praesent viverra volutpat turpis, nec porttitor orci vulputate porta. Nulla fermentum aliquet placerat.

Yu Cheng*, Jian Zhao*, Zhecan Wang, Yan Xu, Jayashree Karlekar, Shengmei Shen, Jiashi Feng
Workshop on MS-Celeb-1M Challenge with ICCV 2017, 2017.10

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam varius felis aliquet, fermentum nisl at, molestie nisi. In hac habitasse platea dictumst. Maecenas luctus lectus eros, vitae malesuada risus cursus porta. Aliquam non enim malesuada quam lobortis lobortis. Fusce ac sapien volutpat, pulvinar ligula sed, sagittis arcu. Nam sagittis sem sit amet vulputate mollis. Donec non mattis lectus, quis tincidunt quam. Phasellus elementum pharetra tellus imperdiet elementum. Maecenas tempor efficitur quam, in mattis velit congue eu. Etiam egestas ultrices consequat. Sed a nisl tellus. Donec orci diam, lobortis ac orci et, eleifend posuere lectus. Etiam sollicitudin turpis a augue maximus tempus. Sed egestas mauris non elit congue, a varius mi elementum. Aliquam at ligula ante. Fusce imperdiet sapien ac dapibus fringilla.

Donec in arcu tortor. Vivamus eleifend lorem a fringilla malesuada. Praesent vitae mauris vehicula, vulputate dui non, consequat quam. Integer tellus diam, ultrices et iaculis eget, rhoncus in odio. Mauris vel aliquam lorem. Maecenas ac hendrerit ligula. Sed quis mi id elit faucibus viverra. Duis ac justo ac turpis blandit hendrerit. Donec lacus massa, blandit in quam non, maximus finibus diam.

Integer a felis massa. Nunc ac maximus tortor. Aliquam velit augue, tincidunt suscipit semper eget, imperdiet ut risus. Suspendisse sit amet porttitor sapien. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Quisque rhoncus tristique euismod. Fusce pharetra, augue nec auctor faucibus, diam orci auctor tortor, eu gravida magna neque non dolor.

Jianan Li, Xiaodan Liang, Shengmei Shen, Jiashi Feng, Shuicheng Yan
IEEE Transactions on Multimedia Volume: 20 , Issue: 4 , April 2018

In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech, and obtains competitive results on INRIA, ETH, and KITTI.

Link: https://ieeexplore.ieee.org/document/8060595

Keke Liu, Yazhou Liu, Quansen Sun, Sugiri Pranata, Shengmei Shen
Pacific-Rim Conference On Multimedia (PCM) 2017, 2017.09

Driver head analysis is of paramount interest for the advanced driver assistance systems (ADAS). Recently proposed methods almost rely on training with labeled samples, especially deep learning. However, the labeling process is a subjective and tiresome manual task. Even trickier, our application scene is driver assistance systems, where the training dataset is more difficult to capture. In this paper, we present a rendering pipeline to synthesize virtual-world driver head pose and facial landmark dataset with annotation by computer 3D animation software, in which we consider driver’s gender, dress, hairstyle, hats and glasses. This large amounts of virtual-world labeled dataset and a small amount of real-world labeled dataset are trained together firstly by deeply supervised transfer metric learning method. We treat it as a cross-domain task, the labeled virtual data is a source domain and the unlabeled real-world data is a target domain. By exploiting the feature self-learning characteristic of deep networks, we find the common feature subspace between them, and transfer discriminative knowledge from the labeled source domain to the labeled target domain. Finally we employ a small number of real-world dataset to fine-tune the model iteratively. Our experiments show high accuracy on real-world driver head images.

Link: https://link.springer.com/chapter/10.1007/978-3-319-77383-4_28

Choon-Ching Ng, Moi Hoon Yap, Yi-Tseng Cheng, Gee-Sern Hsu
Image and Vision Computing Volume 69, January 2018, Pages 92-102, 2017.09

Wrinkles can be embedded in several image-based applications as a descriptor for human skin. However, wrinkle-based age estimation research has not been widely addressed. In this paper, we introduce a Multi-scale Wrinkle Patterns (MWP) representation, investigate the effect of wrinkles on face age estimation and propose Hybrid Ageing Patterns (HAP) for face age estimation. To define the wrinkle regions more precisely, a template consisting of 10 regions constructed relatively to a set of automatically located facial landmarks is used. We extract the multi-scale wrinkles in each region and encode them into MWP. We use Support Vector Regression to estimate age from the combination of such patterns. The performance of the algorithms is assessed by using Mean Absolute Error (MAE) on three state-of-the-art datasets – FG-NET, FERET and MORPH. We observe that MWP produces a comparable MAE of 4.16 on FERET to the state of the art. Finally we propose HAP, which combines the features from MWP and the facial appearance model (FAM), and demonstrate improved performance on FERET and MORPH with MAE of 3.02 (±2.92) and 3.68 (±2.98), respectively. Therefore, we conclude that MWP is an important complementary feature for face age estimation.

Link: https://www.sciencedirect.com/science/article/pii/S026288561730121X

Gee-Sern Jison Hsu ; Yi-Tseng Cheng ; Choon Ching Ng ; Moi Hoon Yap
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.07

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam varius felis aliquet, fermentum nisl at, molestie nisi. In hac habitasse platea dictumst. Maecenas luctus lectus eros, vitae malesuada risus cursus porta. Aliquam non enim malesuada quam lobortis lobortis. Fusce ac sapien volutpat, pulvinar ligula sed, sagittis arcu. Nam sagittis sem sit amet vulputate mollis. Donec non mattis lectus, quis tincidunt quam. Phasellus elementum pharetra tellus imperdiet elementum. Maecenas tempor efficitur quam, in mattis velit congue eu. Etiam egestas ultrices consequat. Sed a nisl tellus. Donec orci diam, lobortis ac orci et, eleifend posuere lectus. Etiam sollicitudin turpis a augue maximus tempus. Sed egestas mauris non elit congue, a varius mi elementum. Aliquam at ligula ante. Fusce imperdiet sapien ac dapibus fringilla.

Donec in arcu tortor. Vivamus eleifend lorem a fringilla malesuada. Praesent vitae mauris vehicula, vulputate dui non, consequat quam. Integer tellus diam, ultrices et iaculis eget, rhoncus in odio. Mauris vel aliquam lorem. Maecenas ac hendrerit ligula. Sed quis mi id elit faucibus viverra. Duis ac justo ac turpis blandit hendrerit. Donec lacus massa, blandit in quam non, maximus finibus diam.

Integer a felis massa. Nunc ac maximus tortor. Aliquam velit augue, tincidunt suscipit semper eget, imperdiet ut risus. Suspendisse sit amet porttitor sapien. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Quisque rhoncus tristique euismod. Fusce pharetra, augue nec auctor faucibus, diam orci auctor tortor, eu gravida magna neque non dolor.

Integer quis accumsan arcu. Pellentesque posuere faucibus risus. Fusce sed feugiat turpis. Vivamus euismod ex vitae lorem dapibus, id efficitur ligula pellentesque. Nulla facilisi. Maecenas luctus nisl at ornare venenatis. Mauris maximus nisl mauris.

Suspendisse a ultrices ipsum, at venenatis risus. Praesent ornare enim purus, ac varius leo convallis et. Nam porta, justo a hendrerit suscipit, sem enim cursus arcu, ac pellentesque eros felis a lacus. Vivamus tristique, ex sed blandit feugiat, ex nunc rutrum felis, in efficitur ante lectus vel massa. Praesent molestie finibus laoreet. Sed ultrices, eros eu cursus finibus, est libero sollicitudin ex, ac cursus lacus lectus ac purus. Donec bibendum vitae velit vitae aliquet. Fusce volutpat finibus tempor. Integer vulputate porta eleifend. Etiam mattis nibh ut aliquet consectetur. Nulla pharetra lectus eget aliquam tempus. Nulla facilisi. Nulla ultricies nisi vel urna posuere, eu porttitor erat maximus. Praesent viverra volutpat turpis, nec porttitor orci vulputate porta. Nulla fermentum aliquet placerat.

G. Chi, K.L. Loi, P. Lasang
Proc. of the 11th International Conference on Computer Vision Systems

Gee-Sern Jison Hsu ; Yi-Tseng Cheng ; Choon Ching Ng ; Moi Hoon Yap
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.07

We propose the Component Bio-Inspired Feature (CBIF) with a moving segmentation scheme for age estimation. The CBIF defines a superset for the commonly used Bio-Inspired Feature (BIF) with more parameters and flexibility in settings, resulting in features with abundant characteristics. An in-depth study is performed for the determination of the parameters good for capturing age-related traits. The moving segmentation is proposed to better determine the age boundaries good for age grouping, and improve the overall performance. The proposed approach is evaluated on two common benchmarks, FG-NET and MORPH databases, and compared with contemporary approaches to demonstrate its efficacy.

Link: https://ieeexplore.ieee.org/abstract/document/8014815