119,748 research outputs found
Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition
In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion
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
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
- …