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We propose a novel method for extracting features from images of people using co-occurrence attributes, which are then used for person re-identification. Existing methods extract features based on simple attributes such as gender, age, hair style, or clothing. Our method instead extracts more informative features using co-occurrence attributes, which are combinations of physical and adhered human...
This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target...
We present a method for combining the Vector of Locally Aggregated Descriptor (VLAD) feature encoding with Deep Convolutional Neural Network (DCNN) features for unconstrained face verification. One of the key features of our method, called the VLAD-encoded DCNN (VLAD-DCNN) features, is that spatial and appearance information are simultaneously processed to learn an improved discriminative representation...
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...
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