The convolutional neural networks (CNN) like AlexNet, GoogleNet, VGGNet, etc. have been proven as the very discriminative feature descriptor for many computer vision problems. The trained CNN model over one dataset performs reasonably well over another dataset of similar type and outperforms the hand-designed feature descriptor. The Rectified Linear Unit (ReLU) layer discards some information in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at last few layers. Basically, AB-ReLU improves the discriminative ability by two ways: 1) it also exploits some of the discriminative and discarded negative information of ReLU and 2) it kills the irrelevant and positive information used by ReLU. The VGGFace model already trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging unconstrained and robust face datasets like PubFig, LFW, PaSC, AR, etc. in retrieval framework. It is observed that AB-ReLU is consistently performed better than ReLU using VGGFace pretrained model over face datasets.
In this work, the original pre-trained VGGFace model is taken from MatConvNet library @cite_20 released by University of Oxford @math @math . This model is based on the CNN implementation of VGG-Very-Deep-16 CNN architecture as described in @cite_44 . This model is trained over VGGFace database @math @math which consists 2.6M faces images from 2,622 subjects. The layers of VGGFace model are summarized in Table . In this table, the last fully connected layer and sofmax layer of VGGFace are not listed as it is not required in this work. The output of relu7' is considered as the VGGFace feature descriptor. The filter size, stride and padding are mentioned in the Filter column with fields @math , @math and @math respectively. A filter size @math :3,128,256 means total 256 filters of dimension 3 @math 3 and depth 128. Similarly, a volume size 112,64 means a 3-D volume of dimension 112 @math 112 with depth 64. In this work, the changes are made in selected rectified linear unit (ReLU) layers, especially in last few layers which is described in the next section.
@cite_44 @cite_20
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The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks. MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing linear convolutions with filter banks, feature pooling, and many more. In this manner, MatConvNet allows fast prototyping of new CNN architectures; at the same time, it supports efficient computation on CPU and GPU allowing to train complex models on large datasets such as ImageNet ILSVRC. This document provides an overview of CNNs and how they are implemented in MatConvNet and gives the technical details of each computational block in the toolbox.