Generation of Paths in a Maze using a Deep Network without Learning

Trajectory- or path-planning is a fundamental issue in a wide variety of applications. Here we show that it is possible to solve path planning for multiple start- and end-points highly efficiently with a network that consists only of max pooling layers, for which no network training is needed. Different from competing approaches, very large mazes containing more than half a billion nodes with dense obstacle configuration and several thousand path end-points can this way be solved in very short time on parallel hardware

Convolutional neural networks for face recognition and finger-vein biometric identification

The Convolutional Neural Network (CNN), a variant of the Multilayer Perceptron (MLP), has shown promise in solving complex recognition problems, particularly in visual pattern recognition. However, the classical LeNet-5 CNN model, which most solutions are based on, is highly compute-intensive. This CNN also suffers from long training time, due to the large number of layers that ranges from six to eight. In this research, a CNN model with a reduced complexity is proposed for application in face recognition and finger-vein biometric identification. A simpler architecture is obtained by fusing convolutional and subsampling layers into one layer, in conjunction with a partial connection scheme applied between the first two layers in the network. As a result, the total number of layers is reduced to four. The number of feature maps at each layer is optimized according to the type of image database being processed. Consequently, the numbers of network parameters (including neurons, trainable parameters and connections) are significantly reduced, essentially increasing the generalization ability of the network. The Stochastic Diagonal Levenberg-Marquadt (SDLM) backpropagation algorithm is modified and applied in the training of the proposed network. With this learning algorithm, the convergence rate is accelerated such that the proposed CNN converges within 15 epochs. For face recognition, the proposed CNN achieves recognition rates of 100.00% and 99.50% for AT&T and AR Purdue face databases respectively. Recognition time on the AT&T database is less than 0.003 seconds. These results outperform previous existing works. In addition, when compared with the other CNN-based face recognizer, the proposed CNN model has the least number of network parameters, hence better generalization ability. A training scheme is also proposed to recognize new categories without full CNN training. In this research, a novel CNN solution for the finger-vein biometric identification problem is also proposed. To the best of knowledge, there is no previous work reported in literature that applied CNN for finger-vein recognition. The proposed method is efficient in that simple preprocessing algorithms are deployed. The CNN design is adapted on a finger-vein database, which is developed in-house and contains 81 subjects. A recognition accuracy of 99.38% is achieved, which is similar to the results of state-of-the-art work. In conclusion, the success of the research in solving face recognition and finger-vein biometric identification problems proves the feasibility of the proposed CNN model in any pattern recognition system

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval

Gender Classification: A Convolutional Neural Network Approach

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition

The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition.

Funder: Istituto Italiano di TecnologiaCurrent state-of-the-art models for automatic facial expression recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and, thus, improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapts the learned facial features towards the different datasets