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Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks are more receptive to pose and illumination others appear to capture more local information. Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features...
In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually...
In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder...
In this paper, we present an end-to-end system for the unconstrained face verification problem based on deep convolutional neural networks (DCNN). The end-to-end system consists of three modules for face detection, alignment and verification and is evaluated using the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version Janus Challenging set 2 (JANUS CS2) dataset. The IJB-A...
Optical coherence tomography (OCT) is an emerging technique for imaging skin and retina. However, severe noise and incompatible dynamic range between OCT image and display devices make OCT image analysis a challenging task. To address these problems, we present a structure-preserving multi-scale decomposition approach. Instead of outputting single image, it represents an OCT image by a set of images...
We present a vision-based human computer interaction system which consists of only one projector and one camera. The proposed system is able to convert an arbitrary flat surface into a touch-screen. It allows user to interact with a computer by the fingertip on any flat surface anywhere and anytime. We design a vision-based algorithm that can identify fingertip and detect touch processing with a precision...
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