Face Gender Classification Research Articles

Deep learning based features extraction for facial gender classification using ensemble of machine learning technique

Deep learning based features extraction for facial gender classification using ensemble of machine learning technique

Stacked ensemble learning for facial gender classification using deep learning based features extraction

Stacked ensemble learning for facial gender classification using deep learning based features extraction

Face-based Gender Classification Using Deep Learning Model

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer learning, a powerful deep learning technique that can be effectively employed to Face gender classification using the Alex-Net architecture. The performance evaluation of the proposed gender classification model encompassed three datasets: the LFW dataset, which contained 1,200 facial images. The Faces94 dataset contained 400 facial images, and the family dataset had 400. The Transfer Learning with the Alex-Net model achieved an accuracy of 98.77% on the LFW dataset. Furthermore, the model attained an accuracy rate of 100% on both the Faces94 and family datasets. Thus, the proposed system emphasizes the significance of employing pre-processing techniques and transfer learning with the Alex-Net model. These methods contribute to more accurate results in gender classification. Where, the results achieved by applying image contrast enhancement techniques, such as HE and CLAHE, were compared. CLAHE achieved the best facial classification accuracy compared to HE.

Defusing Dirty Label Backdoor Attack with Differentially Private Data through Feature Vectorized AlexNet for Face Gender Classification

Defusing Dirty Label Backdoor Attack with Differentially Private Data through Feature Vectorized AlexNet for Face Gender Classification

A SURVEY OF FACIAL GENDER CLASSIFICATION

: Gender is the most critical demographic attribute of human beings. Gender classification from facial images is widely used in computer vision for surveillance, a straightforward task for humans, and a challenging task for machines. This paper provides a study of facial gender classification in the field of computer vision. We highlight the challenges faced during the image capturing process due to factors such as illumination, angle, occlusion, and expressions. We also review various feature extraction approaches used by previous researchers to perform the facial gender classification task. This paper also compares the performances of previous methods on various face datasets to perform gender classification. We observed from the previous studies that good performance had been achieved for a dataset of facial images taken in a controlled environment. However, much more work needs to be done to increase the accuracy and robustness of facial gender classification, especially in uncontrolled environments.

Face Gender Classification by Using Improve Binary Particle Swarm Optimization and K-Nearest Neighbors

This paper studies the application of the Binary particle swarm optimization (BPSO) algorithm to the optimal search for facial features and gender classification by K-Nearest Neighbors (K-NN) model. The results show that the accuracy and processing time of the model is much better than that of VGG16, VGG19, Resnet50, Senet50, Face Net, Open Face and FbDeep Face models. a large-scale GenderFace80K dataset with 80,000 facial images with gender annotation used in the research model.

Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning

Automated facial gender and age classification has remained a challenge because of the high inter-subject and intra-subject variations. We addressed this challenging problem by studying multi-instance- and multi-scale-enhanced multi-task random forest architecture. Different from the conventional single facial attribute recognition method, we designed effective multi-task architecture to learn gender and age simultaneously and used the dependency between gender and age to improve its recognition accuracy. In the study, we found that face gender has a great influence on face age grouping; thus, we proposed a random forest face age grouping method based on face gender conditions. Specifically, we first extracted robust multi-instance and multi-scale features to reduce the influence of various intra-subject distortion types, such as low image resolution, illumination and occlusion, etc. Furthermore, we used a random forest classifier to recognize facial gender. Finally, a gender conditional random forest was proposed for age grouping to address inter-subject variations. Experiments were conducted by using two popular MORPH-II and Adience datasets. The experimental results showed that the gender and age recognition rates in our method can reach 99.6% and 96.14% in the MORPH-II database and 93.48% and 63.72% in the Adience database, reaching the state-of-the-art level.

Cosmetic makeup enhances facial attractiveness and affective neural responses.

Although it is well established that cosmetic makeup enhances perceived facial attractiveness, few studies have examined whether facial makeup modulates neural responses to face images. This study investigated behavioral and attractiveness-related brain responses to self-applied makeup, focusing on the N170, early posterior negativity, P300, and late positive potential components of event-related brain potentials. A total of 77 Japanese women participated in two experiments (N = 34 and 43 for Experiments 1 and 2, respectively). Experiment 1 assessed the effects of self-applied makeup on attractiveness-related event-related potential amplitudes using facial images during a makeup identification task in which makeup was directly relevant to task demands. Experiment 2 examined the effects of self-applied makeup using images of one's own face and another female's face when performing a gender classification task, where the presence of makeup had no explicit connection to facial gender classification. In both experiments, faces with makeup were rated as more attractive and elicited more negative early posterior negativity and more positive late positive potential components, regardless of the participant's own face or another person's face. These findings suggest that people are spontaneously motivated to pay visual attention to faces with makeup, which supports the idea that makeup adds reward value to the facial appearance of the human. Moreover, neural evidence empirically confirmed that the benefits of makeup are not just limited to how others see your face but also extend to how you see your own face.

Implementation-Independent Representation for Deep Convolutional Neural Networks and Humans in Processing Faces.

Deep convolutional neural networks (DCNN) nowadays can match human performance in challenging complex tasks, but it remains unknown whether DCNNs achieve human-like performance through human-like processes. Here we applied a reverse-correlation method to make explicit representations of DCNNs and humans when performing face gender classification. We found that humans and a typical DCNN, VGG-Face, used similar critical information for this task, which mainly resided at low spatial frequencies. Importantly, the prior task experience, which the VGG-Face was pre-trained to process faces at the subordinate level (i.e., identification) as humans do, seemed necessary for such representational similarity, because AlexNet, a DCNN pre-trained to process objects at the basic level (i.e., categorization), succeeded in gender classification but relied on a completely different representation. In sum, although DCNNs and humans rely on different sets of hardware to process faces, they can use a similar and implementation-independent representation to achieve the same computation goal.

Perceptual Oscillations in Gender Classification of Faces, Contingent on Stimulus History.

SummaryPerception is a proactive ‘‘predictive’’ process, in which the brain takes advantage of past experience to make informed guesses about the world to test against sensory data. Here we demonstrate that in the judgment of the gender of faces, beta rhythms play an important role in communicating perceptual experience. Observers classified in forced choice as male or female, a sequence of face stimuli, which were physically constructed to be male or female or androgynous (equal morph). Classification of the androgynous stimuli oscillated rhythmically between male and female, following a complex waveform comprising 13.5 and 17 Hz. Parsing the trials based on the preceding stimulus showed that responses to androgynous stimuli preceded by male stimuli oscillated reliably at 17 Hz, whereas those preceded by female stimuli oscillated at 13.5 Hz. These results suggest that perceptual priors for face perception from recent perceptual memory are communicated through frequency-coded beta rhythms.

Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.

Learning discriminative subregions and pattern orders for facial gender classification

Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based feature vector selection scheme, is then developed to highlight the gender characteristics. As a result, the discriminative subregions and pattern orders are constructed as the feature representation for facial gender classification. We evaluate our proposed method on four benchmark datasets (i.e., FRGC 2.0, FERET, LFW and UND) for gender classification and demonstrate its interpretability, effectiveness and efficiency compared with state-of-the-art methods.

Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning.

FMRI gender classification of faces, bodies, and common names in the left occipitotemporal cortex

fMRI gender classification of faces, bodies, and common names in the left occipitotemporal cortex

A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors, based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database (4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.

Age and gender classification in the wild with unsupervised feature learning

Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

Multi-quantized local binary patterns for facial gender classification

This paper presents multi-quantized local binary patterns for facial gender classification. For encoding the gray level difference (GLD) between a reference pixel and its neighbors, local binary pattern employs a binary quantization which retains the sign of GLD but discards the magnitude information. To improve the discrimination capability, the proposed method utilizes both the sign and magnitude components by performing multi-level vector quantization of GLD. Each quantized level is then separately encoded to generate multiple local binary patterns. The proposed method is evaluated on four publicly available datasets (FERET, PAL, CASIA and FEI) through extensive experiments. Comparison of performance with various existing methods clearly demonstrated that the proposed method has advantages such as higher discrimination power, improved noise robustness and better generalization capability.

On using periocular biometric for gender classification in the wild

Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem. As expected, the achieved results are slightly worse, roughly 8 percentage points lower, than those obtained by state-of-the-art facial GC, but they suggest the validity of the periocular area particularly in difficult scenarios where the whole face is not visible, or has been altered. A final experiment combines in a multi-scale approach features extracted from the periocular, face and head and shoulders areas, fusing them in a two stage ensemble of classifiers. The accuracy reported beats any previous results on the difficult The Images of Groups dataset, reaching 92.46%, with a GC error reduction of almost 20% compared to the best face based GC results in the literature.

Facial Gender Classification with Improved Local Pattern

Facial Gender Classification with Improved Local Pattern

Local circular patterns for multi-modal facial gender and ethnicity classification

Gender and ethnicity are both key demographic attributes of human beings and they play a very fundamental and important role in automatic machine based face analysis, therefore, there has been increasing attention for face based gender and ethnicity classification in recent years. In this paper, we present an effective and efficient approach on this issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the existing ones that only depend on either 2D texture or 3D shape of faces. In order to comprehensively represent the difference between different genders or ethnicities, we propose a novel local descriptor, namely local circular patterns (LCP). LCP improves the widely utilized local binary patterns (LBP) and its variants by replacing the binary quantization with a clustering based one, resulting in higher discriminative power as well as better robustness to noise. Meanwhile the following Adaboost based feature selection finds the most discriminative gender- and race-related features and assigns them with different weights to highlight their importance in classification, which not only further raises the performance but reduces the time and memory cost as well. Experimental results achieved on the FRGC v2.0 and BU-3DFE datasets clearly demonstrate the advantages of the proposed method.