Human Gender Detection from Facial Images Using Convolution Neural Network

Sumi, Tahmina Akter; Hossain, Mohammad Shahadat; Islam, Raihan Ul; Andersson, Karl

doi:10.1007/978-3-030-82269-9_15

Cited by 20 publications

(13 citation statements)

References 27 publications

(25 reference statements)

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“…Result of CNN. However, utilizing hybrid feature vectors into classification attained lower accuracy (95%) than applying the geodesic technique to extract features, see Table 2 [20] 85.5% Singh et al [21] 95.5% Bekhouche et al [4] 88.8 Balci and Atalay [22] 92% Abdelkader and Griffin [23] 85% Makenen et al [24] 84% Yang et al [25] 93% Agbo-Ajala and Viriri [27] 89.7% Khalifa et al [28] 98.88% Haider et al [29] 98% Duan et al [30] 88.2% Tilki et al [31] 92.4% Sumi et al [32] 97.44% Dhomne et al [ 96.5% Hosoi et al [10] 94.33% Roomi et al [11] 81% Biag et al [12] 84.91% Khan et al [13] 93.2, 92, 99.2, 100 Masood et al [14] 98.6% Vo and Le [15] 91% Greco et al [16] 94.1% Heng et al [17] 95.2% Proposed method 100%…”

Section: Discussionmentioning

confidence: 99%

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Gender and race classification using geodesic distance measurement

Marzoog

Hasan

2022

IJEECS

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<span lang="EN-US">Gender and ethnicity classifications are a long-standing challenge in the face recognition’s field. They are key-demographic traits of individuals and applied in real-world applications such as biometric and demographic research, human-computer interaction (HCI), law enforcement and online advertisements. Thus, many methods have been proposed to address gender or/and race classifications and achieved various accuracies. This research improves race and gender classification by employing a geodesic path algorithm to extract discriminative features of both gender and ethnicity. PCA is also utilized for dimensionality reduction of Gender-feature and race-feature matrices. KNN and SVM are used to classify the extracted feature. This research was tested on the face recognition technology (FERET) dataset, with classification results demonstrating high-level performance (100%) in distinguishing gender and ethnicity.</span>

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Section: Discussionmentioning

confidence: 99%

“…It achieved better accuracy when using the proposed CNN (92.4%) compared with AlexNet (90.5%). Sumi et al[32] also suggested a new CNN model for feature extraction from face images and gender classification. They passed face images through convolution layers, RELU and max-pooling layer to extract features.…”

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confidence: 99%

Gender and race classification using geodesic distance measurement

Marzoog

Hasan

2022

IJEECS

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“…For Gender Recognition, various techniques, such as facial photographs [6], hand images, and pose/body images [7], can be used to identify gender. By extracting two different sorts of characteristics, gender recognition is accomplished [8,10].…”

Section: Literature Surveymentioning

confidence: 99%

Gender Recognition and Age Detection Using Human Facial Features

Shah¹,

Malamdi²,

Dave³

et al. 2023

IJSREM

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One of the active study fields in recent years is the extraction of information from the human face. Numerous investigations on the identification of the most prevalent face variety, including age and gender, have been done. The approach for automatically determining a person's age and gender from their face is suggested in this paper and is based on convolutional neural networks and support vector machines. The four steps of this method are face detection, pre-processing, feature extraction, and classification. A variety of convolutional neural networks have been trained to detect faces in live feed and also determine their age and gender are part of our system. Our model was trained using Adience benchmark dataset for age and gender prediction available on Kaggle, which has produced positive results. Keywords: Convolution neural network, Support Vector Machine, Face Detection, Multi-Task Cascaded Convolution Neural Networks, Principal Component Analysis, Adience benchmark dataset.

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“…Comparing the proposed method with some of the previous techniques for gender classification is listed in Table 12 for face images and Table 13 for eyes images. (Sumi et al, 2021) Kaggle dataset, Nottingham scan 97.44,90 Deep CNN (Yildiz et al, 2021) Adience, VGGFace2 85.52,93.71 Pareto frontier CNN (Islam et al, 2020) WIKI-cleaned 90 Pre-trained CNN (Zhou et al, 2019) Adience 93.22 VGGNet arch (Dhomne et al, 2018) Celebrity faces 95 Hyper face (Ranjan et al, 2017) CelebA, LFWA 98,94 Face tracer (Kumar et al, 2008) CelebA, LFWA 84,91 Deep CNN (Kamaru, 2020) CelebA, LFWA 96,95 Deep CNN (Benkaddour et al, 2021) WIKI, IMDB 93.56,94.49 CNN + ELM (Extreme Learning Machine) (Micheala and Shankar, 2021) Adience 90.2 LMTCNN (Lightweight Multi-task CNN) (Lee et al, 2018) Adience 85 Wide CNN + Gabor Filter (Hosseini et al, 2018) Adience 88.9 2DPCA on real Gabor space + SVM (Rai and Khanna, 2015) LFW…”

Section: Train the Cnn Model On Eyes From Different Datasetsmentioning

confidence: 99%

Gender Classification from Face and Eyes Images Using Deep Learning Algorithm

Ghrban¹,

Abbadi²

2023

Journal of Computer Science

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Gender classification provides additional information about the individual's identity, which is crucial in surveillance, smart interface, and smart advertising, it is a very important aspect of face analysis that has piqued the interest of researchers in areas such as demographic information collection, surveillance, human-computer interaction, marketing intelligence, security, etc. Usually, facial images are used to extract features for classification. This study aims to determine gender based on the entire face or the eyes (for masked or occlusion faces). The proposed method consists of six main stages: (1) Remove the background using the Deep Labelling Version 3 plus (DeepLabV3+) method; (2) Skin detection by applying the combination of the two colors spaces models (Hue, Saturation, Value (HSV)) and (Luminance, Chrominance blue and Chrominance red (YCbCr)); (3) Face detection using the Haar Cascades classifier method; (4) Face alignment and cropping; (5) Classify the gender. Classification of the gender when the image contains the entire face is based on a deep wavelet. In the case of occlusion faces, we proposed to use a Convolution Neural Network (CNN) for classifying the gender from the eye (s). The dataset used for training this model is the celeb faces attributes dataset (CelebA) and some of the different datasets were also used to compare this model with the other previous works. In addition, we showed that our models perform well with images with some challenges, for example, some of the faces are not fully visible, not completely frontal, wearing glasses with different styles, have low quality and noise, have various lighting, children's and infants' faces, with and without makeup, open and closed mouths and closed eyes. Other than that, the accuracy achieved was 98% when classifying from face images and 98% from the eye. The proposed method was compared with previous works and was very promising. The contributions of this proposal were the use of the deep wavelet for gender classification and the proposal of a new method for skin detection, which enhances the performance of the Haar cascade method used for face detection. Also, we proposed a method for face alignment and, finally, the classification of genders with many challenges from the entire face or just from the eye (s).

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Human Gender Detection from Facial Images Using Convolution Neural Network

Cited by 20 publications

References 27 publications

Gender and race classification using geodesic distance measurement

Gender and race classification using geodesic distance measurement

Gender Recognition and Age Detection Using Human Facial Features

Gender Classification from Face and Eyes Images Using Deep Learning Algorithm

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