10,434 research outputs found
DCTNet : A Simple Learning-free Approach for Face Recognition
PCANet was proposed as a lightweight deep learning network that mainly
leverages Principal Component Analysis (PCA) to learn multistage filter banks
followed by binarization and block-wise histograming. PCANet was shown worked
surprisingly well in various image classification tasks. However, PCANet is
data-dependence hence inflexible. In this paper, we proposed a
data-independence network, dubbed DCTNet for face recognition in which we adopt
Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is
motivated by the fact that 2D DCT basis is indeed a good approximation for high
ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated
sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is
free from learning as 2D DCT bases can be computed in advance. Besides that, we
also proposed an effective method to regulate the block-wise histogram feature
vector of DCTNet for robustness. It is shown to provide surprising performance
boost when the probe image is considerably different in appearance from the
gallery image. We evaluate the performance of DCTNet extensively on a number of
benchmark face databases and being able to achieve on par with or often better
accuracy performance than PCANet.Comment: APSIPA ASC 201
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
On the use of SIFT features for face authentication
Several pattern recognition and classification techniques
have been applied to the biometrics domain. Among them,
an interesting technique is the Scale Invariant Feature
Transform (SIFT), originally devised for object recognition.
Even if SIFT features have emerged as a very powerful image
descriptors, their employment in face analysis context
has never been systematically investigated.
This paper investigates the application of the SIFT approach
in the context of face authentication. In order to determine
the real potential and applicability of the method,
different matching schemes are proposed and tested using
the BANCA database and protocol, showing promising results
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