Gender recognition using innovative pattern recognition techniques

Kabasakal, B.V.; Sümer, Emre

doi:10.1109/siu.2018.8404306

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Soft computing, based on single biometric information cannot deal with spoofing attacks, large datasets and unacceptable errors, and on the other hand, hard computing alone cannot be used for many applications [ 7 ]. Based on that, we proposed a system that includes facial features, gender information and age information and it will also deal with spoofing attacks [ 8 , 9 , 10 ]. In other words, the proposed system is a combination of soft and hard biometrics which associate a multi-biometric system to enhance the accuracy of the proposed system.…”

Section: Introductionmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

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Section: Introductionmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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“…Other authors have used ensemble learning [58] and K-nearest neighbor (KNN) [63] methods. The studies [21,[32][33][34]37,38] are most similar to our study, wherein the main aim is to compare the performances of different features and machine learning models for the task of gender recognition. The studies [33] and [34] investigated the use of deep-learned features with the CNN and SVM models; they report contradictory findings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Gender recognition is a domain where high state-of-the-art accuracy has been achieved by SVMs and CNNs [21,33,34,37,38]. These results, however, have been attributed to the characteristics of the dataset used [17,21,22,31].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hosseini et al [32] showed that feeding hand-crafted features to CNN can improve their performance. The SVM performances with hand-crafted features and CNNs with deep-learned features are explored in [21,37,38,81]; CNNs with deep-learned features achieved the best results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Studies in the field of gender recognition have only focused on comparing the two feature extraction paradigms with one model [32], a single paradigm with multiple models [33,34], or limited feature extraction methods and models [21,37,38,81]. Because of the variations in the experimental setups, the results from different studies cannot be compared.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

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Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.

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Gender Based Access Control System

Dube

2022

2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET

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Gender recognition using innovative pattern recognition techniques

Cited by 4 publications

References 7 publications

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Gender Based Access Control System

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