31,880 research outputs found
Role of color in face recognition
One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar
How Image Degradations Affect Deep CNN-based Face Recognition?
Face recognition approaches that are based on deep convolutional neural
networks (CNN) have been dominating the field. The performance improvements
they have provided in the so called in-the-wild datasets are significant,
however, their performance under image quality degradations have not been
assessed, yet. This is particularly important, since in real-world face
recognition applications, images may contain various kinds of degradations due
to motion blur, noise, compression artifacts, color distortions, and occlusion.
In this work, we have addressed this problem and analyzed the influence of
these image degradations on the performance of deep CNN-based face recognition
approaches using the standard LFW closed-set identification protocol. We have
evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and
GoogLeNet. Results have indicated that blur, noise, and occlusion cause a
significant decrease in performance, while deep CNN models are found to be
robust to distortions, such as color distortions and change in color balance.Comment: 8 pages, 3 figure
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