Bor-Chun Chen

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Recently, promising results have been shown on face recognition researches. However, face recognition and retrieval across age is still challenging. Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. We propose a novel coding framework called Cross-Age(More)
This paper introduces an approach for face cognizance throughout age and in addition a dataset containing variations of age in the wild. We use a data-driven system to deal with the go-age face realization challenge, known as cross-age reference coding (CARC). By using leveraging a colossal-scale snapshot dataset freely available on the web as a reference(More)
Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face(More)
We aim to develop a scalable face image retrieval system which can integrate with partial identity information to improve the retrieval result. To achieve this goal, we first apply sparse coding on local features extracted from face images combining with inverted indexing to construct an efficient and scalable face retrieval system. We then propose a novel(More)
With the explosive growth of camera devices, people can freely take photos to capture moments of life, especially the ones accompanied with friends and family. Therefore, a better solution to organize the increasing number of personal or group photos is highly required. In this paper, we propose a novel way to search for face images according facial(More)
High-dimensional local binary patterns [5] have been proved to be a useful feature for face recognition, which provides near-human performance in a widely used face verification benchmark. In this report, we first review the technical aspect of this promising feature, and then we provide our implementation details of the feature. Finally, we show some(More)
The ubiquitous availability of digital cameras has made it easier than ever to capture moments of life, especially the ones accompanied with friends and family. It is generally believed that most family photos are with faces that are sparsely tagged. Therefore, a better solution to manage and search in the tremendously growing personal or group photos is(More)
This work attempts to tackle the IBM grand challenge - seeing the daily life of New York City (NYC) in various perspectives by exploring rich and diverse social media content. Most existing works address this problem relying on single media source and covering limited life aspects. Because different social media are usually chosen for specific purposes,(More)
Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the fine-grained location information, i.e. business venues, of the image. To this end, we propose a novel framework for(More)
Facial attribute is important information for a variety of machine vision tasks including recognition, classification, and retrieval. There arises a strong need for detecting various facial attributes such as gender, age and more which consume more computation and storage resources. Therefore, we propose a compression framework to find fewer significant(More)