Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet.

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.

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.

In this paper, we propose a novel pattern to represent spatio-temporal information of gait appearance which is called Gait Principal Component Image (GPCI). GPCI is a grey-level image which compresses the spatiotemporal information by amplifying the dynamic variation of different body part. The detection of gait period is based on LLE coefficients and it is also a new attempt. KNN classifier is employed for gender classification. The framework can be applied in real-time setting because of its rapidity and robustness. The experimental results on IRIP Gait Database (32 males, 28 females) show that the proposed approach achieves a high accuracy in automatic gender classification.

Emotion, age, and gender classification in children’s speech by humans and machines

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

This paper presents a thorough study into the influence of the image resolution on automatic face gender classification. The images involved range from extremely low resolutions (2 × 1 pixels) to full face sizes (329 × 264 pixels). A comprehensive comparison of the performances achieved by two classifiers using ten different image sizes is provided by means of two performance measures Correct Classification Rate (CCR) and Geometric Mean (GMean). Single- and cross-database experiments are designed over three well-known face datasets. A detailed statistical analysis of the results revealed that a face as small as 3 × 2 pixels carries some useful information for distinguishing between genders. However, in situations where higher resolution face images are available, moderately sized faces from 22 × 18 to 90 × 72 pixels are optimal for this task. Furthermore, the performance of the classifiers was robust to the changes in the image resolution (using medium to full sizes). Only when the image resolution was reduced to 8 × 6 pixels or smaller, the classification results were significantly affected.

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

Voice-based age, gender, and language recognition based on ResNet deep model and transfer learning in spectro-temporal domain

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.

ABSTRACTA person’s face provides a lot of information such as age, gender, and identity. Faces play an important role in the estimation/prediction of the age and gender of persons, just by looking at their face. Perceiving human faces and modeling the distinctive features of human faces that contribute most toward face recognition are some of the challenges faced by computer vision and psychophysics researchers. There are many methods have been proposed in the literature for the facial features for age and gender classification. In this research, an attempt is made to classify human age and gender using feed forward propagation neural networks in coarser level. Further final classification is done using 3-sigma control limits in finer level. Proposed approach efficiently classifies three age groups including children, middle-aged adults, and old-aged adults. Similarly two gender groups classified into male and female by the proposed method.The performance of the system is further improved by employing multiple hierarchical decision using three sigma control limits applied on the output of the neural network classifier. The mean and standard deviation has been considered on the output generated from the neural network classifier, and three sigma control limits has been applied to define the range of values for the specific category of age and gender. The efficiency of the system is demonstrated through the experimental results using benchmark database images.

Age and Gender classification of unconstrained imaging conditions has attracted an increased recognition as it is applicable in many real-world applications. Recent deep learning-based methods have shown encouraging performance in this field. We, therefore, propose an end-to-end deep learning-based method for robust age group and gender classification of unconstrained images. Particularly, we address the estimations problem with a classification based model that treats age value as a separate class and an independent label. The proposed deep convolutional neural network model learns the relevant informative age and gender representations directly from the image pixel. Technically, the model is first pre-trained on large-scale IMDb-WIKI facial aging dataset, and then fine-tuned on MORPH-II, another large-scale facial aging dataset to learn, and pick up the bias and particularities of each dataset. Finally, it is fine-tuned on the original dataset (OIU-Adience benchmark) with gender and age group labels. The experimental results when analyzed for estimation accuracy on OIU-Adience dataset, show that our model obtains the state-of-the-art performance in both age group and gender classification with an exact and one-off accuracy of 83.1% and 93.8% on age, and also an exact accuracy of 96.2% on gender.

Gender, makeup, age and illumination classification has become one of the major task and these tasks has become more powerful in real time applications. Still, the performance of existing method is lacking when compared in optimization. In this research paper we have been applied various machine learning models to get the results more optimized. We try to developed ensemble techniques for sturdy age classification ranges and these ranges are classified into four categories that are child, young, middle and old. Similarly Gender classification ranges are divided into male and female and Makeup classification ranges are divided into partial makeup and over makeup and also illumination classification ranges are divided into bad, medium and high categories.

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

In this paper, we demonstrate the need for dimensionality reduction to mitigate model overfitting on the nontrivial problem of gender classification from digital images. In this study we explore four feature selection schemes using Genetic Algorithm, Memetic Algorithms, and Random Forest, which are fed to a nonlinear support vector machine (SVM) for final classification. The performance of the model (feature) selection approaches are evaluated against two distinct datasets of facial images: FG-NET which contains toddlers to seniors and the UIUC-PAL which contains faces of adults up to seniors. This work demonstrates that feature selection can, and does, improve performance of an SVM based gender classification system significantly.

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. In this paper, we propose an approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multilinear principal component analysis is employed to reduce dimensionality of the tensor objects which integrate all views. Compared with other methods, the proposed fusion scheme shows more effective performance for multi-view gait based gender classification.