Neural Network Regression of Eyes Location in Face Images

Rusek, Krzysztof; Guzik, Piotr

doi:10.1007/978-3-642-38559-9_18

Cited by 1 publication

(2 citation statements)

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“…all [16] independently of [13] proposed a similar idea of using convolutional neural networks for facial point detection. The location of the points is given by the first network (regression problem) and it is corrected by another two convolutional networks.…”

Section: Related Workmentioning

confidence: 99%

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Two-stage neural network regression of eye location in face images

Rusek

Guzik

2014

Multimed Tools Appl

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Automatic eye localization is a crucial part of many computer vision algorithms for processing face images. Some of the existing algorithms can be very accurate, albeit at the cost of computational complexity. In this paper, a new solution to the problem of automatic eye localization is proposed. Eye localization is posed as a nonlinear regression problem solved by two feed-forward multilayer perceptrons (MLP) working in a cascade. The input feature vector of the first network is constructed from coefficients of a two dimensional discrete cosine transform(DCT) of a face image. The second network generates corrections based on small image patches. Feature extraction and neural network prediction have known and efficient implementations, thus the entire procedure can be very fast. The paper hints at the neural network structure and the procedure for generating artificial training samples from a low number of face images. In terms of accuracy, the method is comparable to state-of-the-art techniques; however it is based on numerical procedures that could be highly optimized (fast Fourier transform and matrix multiplication).

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Section: Related Workmentioning

confidence: 99%

“…In [13] we present another approach to the problem of eye localization, where the issue is described a one of regression. DCT coefficients were proposed as a feature vector, and an artificial neural network was used for regression.…”

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confidence: 99%