776 research outputs found
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features
Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
network (DNN) resemblance in terms of its very deep, feedforward network
architecture. The typical S-DNN aggregates a variable number of individually
learnable modules in series to assemble a DNN-alike alternative to the targeted
object recognition tasks. This work likewise devises an S-DNN instantiation,
dubbed deep analytic network (DAN), on top of the spectral histogram (SH)
features. The DAN learning principle relies on ridge regression, and some key
DNN constituents, specifically, rectified linear unit, fine-tuning, and
normalization. The DAN aptitude is scrutinized on three repositories of varying
domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10
(natural objects). The empirical results unveil that DAN escalates the SH
baseline performance over a sufficiently deep layer.Comment: 5 page
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
Recognition of Facial Expressions using Local Mean Binary Pattern
In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID
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