Gender classification using image processing techniques: A survey

Khan, Sajid Ali; Nazir, Muhammad; Akram, Sheeraz; Riaz, Naveed

doi:10.1109/inmic.2011.6151483

Cited by 23 publications

(10 citation statements)

References 5 publications

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“…The largest margin is obtained by maximising the distance between the hyperplane and its nearest point, which is 1/∥⃗ w∥. It can be defined as a Quadratic Programming (QP) problem to determine the formula's minimal point (6) and taking into account the constraints of formula (7).…”

Section: ) K-nearest Neighbour (Knn)mentioning

confidence: 99%

“…Therefore, we need the help of a computerised system to observe gender recognition with a particular purpose. Some of the uses of gender classification applications [5] include security system monitoring [6] [7], marketing strategies in shopping centre [8] [9], surveillance for advertising targets [10], and human-computer interaction applications [11] [12]. The fields of science that process biometric data of face [13][14] [15] are usually image processing and computer vision.…”

Section: Introductionmentioning

confidence: 99%

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Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Adhinata¹,

Junaidi²

2022

IJCDS

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Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision. The human face is biometric information that can be used to differentiate gender using a computer-aided system. Previous research utilised a local feature algorithm for extracting features on the face. However, the processing speed for one image was more than 2 seconds, making it unsuitable for real-time processing using video data. Processing video data requires a fast feature extraction algorithm because video data collects sequential images (frames). Moreover, the gender classification system's success is also measured by its accuracy, consequently it is necessary to choose the correct classification method to divide the two classes of men and women. In this research, we propose the FaceNet algorithm for feature extraction and explore several supervised machine learning methods (KNN, SVM, and Decision tree) appropriate for gender classification on video data. This study used 23,000 training data on each gender. From the experiment, combination of the FaceNet algorithm and KNN method resulted in the best accuracy of 95.75% with a processing speed of 0.059 seconds on each frame.

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Section: ) K-nearest Neighbour (Knn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Adhinata¹,

Junaidi²

2022

IJCDS

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“…Taking an overview of these major problems, our research is focused on summarizing the literature by highlighting its strengths and limitations. [6] This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. While there is a large body of research that explores biometrics as a means of verifying identity, there has been very little work done to determine if biometric measures can be used to determine specific human attributes.…”

Section: Literature Surveymentioning

confidence: 99%

Gender Classification From The Iris Code Used For Recognition

KMS¹,

Shanmugan²

2017

IJMSR

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Abstract-In this paper, we present a new method for gender classification based on features of the iris texture selected by mutual information to improve gender classification of face images. The performance of the proposed approach has been investigated through extensive experiments performed on public database. We also showed improved results by fusion of texture features with best features selected independently from the left and right irises based on selection of features using rank feature selection method. The classification task has been achieved by using Support Vector Machine (SVM). We compared our method with existing gender classification methods based on texture feature with classifier being the same as SVM.

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“…Existing overview-articles for algorithms related to gender estimation include the works of Ng et al [31], Khan et al [22], and Bekios-Calfa et al [2].…”

Section: Related Workmentioning

confidence: 99%

Can a Smile Reveal Your Gender?

Biliński

Dantcheva

Brémond

2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Automated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze facial appearance for clues of gender. In this work, we propose a novel approach for gender estimation, based on facial behavior in video-sequences capturing smiling subjects. The proposed behavioral approach quantifies gender dimorphism of facial smiling-behavior and is instrumental in cases of (a) omitted appearance-information (e.g. low resolution due to poor acquisition), (b) gender spoofing (e.g. makeup-based face alteration), as well as can be utilized to (c) improve the performance of appearance-based algorithms, since it provides complementary information. The proposed algorithm extracts spatio-temporal features based on dense trajectories, represented by a set of descriptors encoded by Fisher Vectors. Our results suggest that smile-based features include significant gender-clues. The designed algorithm obtains true gender classification rates of 86.3% for adolescents, significantly outperforming two state-of-the-art appearance-based algorithms (OpenBR and how-old.net), while for adults we obtain true gender classification rates of 91.01%, which is comparably discriminative to the better of these appearance-based algorithms.

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Gender classification using image processing techniques: A survey

Cited by 23 publications

References 5 publications

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Gender Classification From The Iris Code Used For Recognition

Can a Smile Reveal Your Gender?

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