14,617 research outputs found
Face recognition using color local binary pattern from mutually independent color channels
In this paper, a high performance face recognition system based on local
binary pattern (LBP) using the probability distribution functions (PDF) of
pixels in different mutually independent color channels which are robust to
frontal homogenous illumination and planer rotation is proposed. The
illumination of faces is enhanced by using the state-of-the-art technique which
is using discrete wavelet transform (DWT) and singular value decomposition
(SVD). After equalization, face images are segmented by use of local Successive
Mean Quantization Transform (SMQT) followed by skin color based face detection
system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a
given face obtained by LBP and the concatenated PDFs of each face in the
database is used as a metric in the recognition process. Various decision
fusion techniques have been used in order to improve the recognition rate. The
proposed system has been tested on the FERET, HP, and Bosphorus face databases.
The proposed system is compared with conventional and thestate-of-the-art
techniques. The recognition rates obtained using FVF approach for FERET
database is 99.78% compared with 79.60% and 68.80% for conventional gray scale
LBP and Principle Component Analysis (PCA) based face recognition techniques
respectively.Comment: 11 pages in EURASIP Journal on Image and Video Processing, 201
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks independently. In this paper, we propose a novel
end-to-end deep learning framework for joint AU detection and face alignment,
which has not been explored before. In particular, multi-scale shared features
are learned firstly, and high-level features of face alignment are fed into AU
detection. Moreover, to extract precise local features, we propose an adaptive
attention learning module to refine the attention map of each AU adaptively.
Finally, the assembled local features are integrated with face alignment
features and global features for AU detection. Experiments on BP4D and DISFA
benchmarks demonstrate that our framework significantly outperforms the
state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201
Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images
This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.Comment: Accepted to 3DV 201
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