Performance evaluation of enhanced deep learning classifiers for person identification and gender classification

Prior work by a subset of authors led to a development of optical camera platform, which has a capability of distinguishing between human and animal movement in an outdoor environment. Once the image is classified as a human, an idea of providing additional information and insights led to the exploration of gender (men vs women) and direction (left to right vs right to left) classifications. The proposed method classifies the human gender, based on the full body image oriented in a side view manner. An additional feature is to classify the direction of the movement. In the current paper, the Convolutional Neural Networks (CNNs) are used to distinguish between men and women gender classes and to identify the direction of the movement. Furthermore, different aspects of CNN are visualized (for example, attention heat maps, t-Distributed Stochastic Neighbor Embedding plot, etc.) to provide useful insights corresponding to classifications and misclassifications. Additionally, different CNN architectures were tried to figure out the best possible choice. The classification accuracies for gender (men vs women) and direction classification (left to right vs right to left) on the test data are close to 93.3% and 94%, respectively.

Periocular Gender Classification using Global ICA Features for Poor Quality Images

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

We present a convolutional neural network (CNN) approach to age and gender classification, replicat- ing the design of Levi and Hassner [1]. We train a small three-layer CNN on the challenging Adience benchmark of unconstrained face photographs [2]. The network consists of three convolutional layers (with ReLU activations, pool- ing, and normalization) followed by two fully connected layers with dropout, culminating in a softmax output for age (8 classes) or gender (2 classes). Using five-fold cross- validation on the Adience dataset, our model achieves approximately 86.8% accuracy for gender and 50.7% exact (84.7% one-off) accuracy for age. These results match those reported in [1], demonstrating the effectiveness of CNNs for facial attribute estimation in unconstrained images

Online finger-knuckle-print verification for personal authentication

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Age estimation from facial images is one of the most popular fields of research concerning deep learning and convolutional neural networks. However, there are several factors influencing the final accuracy that require special consideration, and in this research, we examine how gender classification affects age estimation. We use a predefined version of the MobileNetV2 convolutional neural network and train it on the CASIAWebFace dataset which we augmented with our private dataset called AgeCFBP. For the purpose of testing the network performance, we used the FG-NET dataset. The results of our experiments showed that gender pre-classification has a measurable impact on age estimation in both male and female population by decreasing Mean Absolute Error (MAE) metric, which might lead to enhanced applications in real-world scenarios, such as biometric authentication, security systems, human-computer interaction, and age-restricted content access control.

Gender classification from human face images has attracted researchers over the past decade. It has great impact in different fields including defense, human-computer interaction, surveillance industry, and mobile applications. Many methods and techniques have been proposed depending on clear digital images and complex feature extraction preprocessing. However, most recent critical real systems use thermal cameras. This paper has the novelty of utilizing thermal images in gender classification. It proposes a unique approach called IRT_ResNet that adopts residual network (ResNet) model with different layer configurations: 18, 50, and 101. Two different datasets of thermal images have been leveraged to train and test these models. The proposed approach has been compared with convolutional neural network (CNN), principal component analysis (PCA), local binary pattern (LBP), and scale invariant feature transform (SIFT). The experimental results show that the proposed model has higher overall classification accuracy, precision, and F-score compared to the other techniques.

With the economic globalization and fast-changing of information technology, the logistics industry and production efficiency have greatly improved, and the logistics distribution have become an important part of the modern logistics industry. The selection of the distribution path in the logistics is the key issue because the choice of reasonable distribution route have great significance on increasing delivery speed, improving service quality, reducing distribution cost and increasing economic benefit. Aiming at this characteristic in the process of logistics distribution, this paper presents an adaptive chaotic disturbance flies optimization algorithm (ACD-FOA), which can optimize fruit flies algorithm in a state of convergence for global optimization. It also can overcome the shortcoming of flies optimization algorithm which is easy to fall into local faults in the solving process to some extent. Based on this advantage, the convergence speed, reliability and optimization on the accuracy of optimization algorithm are much better than the pure flies. Finally the instance simulation test have been carried out, which proved that the adaptive chaotic flies optimization algorithm has better ability of global optimization. In order to prove that the proposed adaptive chaotic disturbance fly optimization algorithm has higher optimization accuracy in logistics distribution, the results were compared with three other models, which indicates that the ACD-FOA established in this paper is more applicable to the current Logistics distribution path optimization. It also can find the optimal logistics distribution path quickly and reduce the logistics distribution cost effectively at the same time.

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Most popular image classification methods mainly focus on classification ability rather than recognizing new things. However, human lay emphasis on cognition first and then classification, which is closely related to human memory system. Though many memory models have been proposed, they are studied in word list whereas the reports about natural images are still limited. This paper proposes a memory model for image recognition and classification based on convolutional neural network and Bayesian decision. First the image feature is extracted by convolutional neural network and stored in binary form. Then the representation, storage and retrieval processes of visual images are modeled. The test image feature vector is matched in parallel to the studied image vectors, and the likelihood values are calculated. Finally, the odd that the test image belongs to a new class is computed based on all likelihood values. If the odd value is greater than a certain threshold, the test image is regarded as new; otherwise, the Bayesian decision rule for image classification is performed. Experimental results on Caltech-101 and Caltech-256 datasets show that the proposed method can perform well in image recognition and classification tasks. And the hit probability of the method is higher than two typical methods, SRC and ELM, at present while the false alarm rate is far lower than them.

Automatic gender classification from speech is an integral component of human-computer interfaces. Gender information is utilized in user authentication, speech recognizers, or human-centered intelligent agents. This study focuses on gender classification from speech spectrograms using AlexNet-inspired 2D convolutional neural networks (CNN) trained on real samples augmented with synthetic spectrograms. A generative adversarial network (GAN) is trained to produce synthetic male/female-like speech spectrograms. In limited training data experiments on LibriSpeech, augmenting a training set of 200 real samples by 800 synthetic samples reduces equal error rate of the classifier from 23.7% to 1.0%. To further test the ‘quality’ of the generated samples, in a subsequent experiment, the real training samples are progressively replaced (rather than augmented) with synthetic samples at various ratios from 0 (all original samples preserved) to 1 (all original samples replaced by synthetic ones). Depending on the system setup, substituting between 50% to 90% of the original samples with the synthetic ones is found to have a minimal impact on the classifier performance. Finally, viewing the input CNN layers as filters that select salient spectrogram features, the learned convolutional kernels and filter outputs are studied to understand which spectrogram areas receive a prominent attention in the classifier.

Objects and processes classification is a common experimental problem. Its solution, first of all, is needed in automatic diagnosis systems, for example, to determine the equipment operation state through the diagnostic signal or to identify abnormalities with medical images. With the development of convolutional neural networks, new prospects for solving such problems have risen up. However, the classification accuracy that can be achieved on these networks is not sufficient enough for all diagnosis issues. It is subject to, for example, timely diagnosis of the onset of transient phenomena. At the same time, another type of neural network, Kohonen self-organizing maps, has a conceptual property for training on unclassified set of classes, that is, giving the opportunity to solve such an issue. Therefore, the accuracy enhancement of the classification on the basis of Kohonen networks implementation in the architecture of convolutional networks is a relevant objective and has practical significance. The article analyzes the means of improving the accuracy of convolutional neural networks and arising problems solution. The ways of increasing the proportion of correct clustering on Kohonen networks due to the ‘growth’ of its grid shape in determining new classes in the learning process are also given. It is this property that makes it possible to recognize transient phenomena. It is determined that the existing solutions of the combination of Kohonen networks and convolutional networks are aimed at improving the efficiency of only self-organizing maps, with the purpose to improve the accuracy of classification by convolutional networks, it became necessary to develop a new architecture. The paper provides a description of this issue. Since the initial information of the Kohonen networks is the weighting matrix values of the grid shape neurons, it was necessary to associate it with the representation of the images in order to process the diagnostic images. The paper proposes the concept of a built-in associative array block based on Kohonen networks. According to the proposed method, a software implementation of a hybrid neural network is developed. The formulation and results of computational experiments are presented. The efficiency of the proposed method is experimentally proved. Keywords: convolutional neural networks, CNN, Kohonen self-organizing maps, SOM, signal classification, image classification.