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

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

Tracking The Eyes Using Interdependence Mean Shift Tracking Algorithm With Appropriate Information Provided

The human eye tracking algorithm is very important, especially in the facial analysis application. Eyes represent rich of information that able to indicate the reaction, perception, or physiological status of individual. Most of the developed eyes tracking algorithm are not considered the condition of the eyes that would provide the appropriate information to be used in the processes of the facial analysis algorithm. This paper proposes a new technique in how the eyes are tracked and provide the appropriate information for further process. This technique uses Mean Shift tracking algorithm and interdependence scheme to track the eye and stop the tracking when eyes are out of best position and condition to deliver the appropriate information. This proposed algorithm is tested and evaluated using Strathclyde Facial Fatigue (SFF) video footage database, a collection of video footage from the internet and our own facial recording. Based on the experiment carried out, the proposed technique shows the promising result

Convolutional Neural Network for Face Recognition with Pose and Illumination Variation

Face recognition remains a challenging problem till today. The main challenge is how to improve the recognition performance when affected by the variability of non-linear effects that include illumination variances, poses, facial expressions, occlusions, etc. In this paper, a robust 4-layer Convolutional Neural Network (CNN) architecture is proposed for the face recognition problem, with a solution that is capable of handling facial images that contain occlusions, poses, facial expressions and varying illumination. Experimental results show that the proposed CNN solution outperforms existing works, achieving 99.5% recognition accuracy on AR database. The test on the 35-subjects of FERET database achieves an accuracy of 85.13%, which is in the similar range of performance as the best result of previous works. More significantly, our proposed system completes the facial recognition process in less than 0.01 seconds

Finger-Vein Biometric Identification Using Convolutional Neural Network

A novel approach using a convolutional neural network (CNN) for finger-vein biometric identification is presented in this paper. Unlike existing biometric techniques such as fingerprint and face, vein patterns are inside the body, making them virtually impossible to replicate. This also makes finger-vein biometrics a more secure alternative without being susceptible to forgery, damage, or change with time. In conventional finger-vein recognition methods, complex image processing is required to remove noise and extract and enhance the features before the image classification can be performed in order to achieve high performance accuracy. In this regard, a significant advantage of the CNN over conventional approaches is its ability to simultaneously extract features, reduce data dimensionality, and classify in one network structure. In addition, the method requires only minimal image preprocessing since the CNN is robust to noise and small misalignments of the acquired images. In this paper, a reduced-complexity four-layer CNN with fused convolutional-subsampling architecture is proposed for finger-vein recognition. For network training, we have modified and applied the stochastic diagonal Levenberg-Marquardt algorithm, which results in a faster convergence time. The proposed CNN is tested on a finger-vein database developed in-house that contains 50 subjects with 10 samples from each finger. An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved

Brain-inspired spiking neural networks for Wi-Fi based human activity recognition

Human activities can be recognized through reflections of wireless signals which solve the problem of privacy concerns and restriction of the application environment in vision-based recognition. Spiking Neural Networks (SNNs) for human activity recognition (HAR) using Wi-Fi signals have been proposed in this work. SNNs are inspired by information processing in biology and processed in a massively parallel fashion. The proposed method reduces processing resources while still maintaining accuracy through using frail but robust to noise spiking signals information transfer. The performance of HAR by SNNs is compared with other machine learning (ML) networks, such as LSTM, Bi-LSTM and GRU models. Significant reduction in memory usage while still having accuracy that is on a par with other ML networks has been observed. More than 70% saving in memory usage has been achieved in SNNs compared with the other existing ML networks, making SNNs a potential solution for edge computing in industrial revolution 4.0

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research

A Development of Electrical Vehicle Charging System Using Wireless Power Transfer

Wireless power transfer is a method of transferring electrical energy from power source to electrical load without any wire connections. It is used to supply the power on electrical devices without any physical connection except air as the medium transfer. In this paper, the design of wireless power transfer charging system for electric vehicles using inductive resonance coupling method will be presented. The purpose of this project is to develop a prototype of electrical vehicle (EV) charging system by wireless power transfer using inductive resonance coupling method. Besides, the performance of wireless power transfer prototype using this method will also investigate. This paper are carried out with the theoretical studies of inductive resonance coupling method, design proposed, circuit simulation and prototype development. The prototype were consists of a few components which are power supply, transmitter/receiver coil with circuits and charging circuit for EV battery. Observation from the prototype experimental show that, power supply can be transfer wirelessly through transmitter and receiver circuit using inductive resonance coupling principles. Other than that, the performance of electrical vehicle charging system are depends on distance of transmitter and receiver coils, period of charging and amount of power sources