Gender classification using face recognition

Bissoon, Terishka; Viriri, Serestina

doi:10.1109/icastech.2013.6707489

Cited by 8 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Facial gender is also classified based on feature dimension reduction techniques as Independent Component Analysis (ICA) with Linear Discriminant Analysis(LDA) classification [49], Principal Component Analysis (PCA) with neural network classification [52], 2D-PCA with SVM classification [69] and PCA with LDA classification [11]. The best results are achieved by Independent Component Analysis (ICA) with LDA classifier on FERET dataset.…”

Section: Conventional Learning Based Facial Gender Recognitionmentioning

confidence: 99%

Review: Single attribute and multi attribute facial gender and age estimation

Gupta

Nain

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment.

show abstract

Section: Conventional Learning Based Facial Gender Recognitionmentioning

confidence: 99%

Review: Single attribute and multi attribute facial gender and age estimation

Gupta

Nain

2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Mohamed et al [19] used DWT and DCT feature extraction techniques with the SVM classifier which outperforms with 95% accuracy on the FERET dataset compared to [18] which used DWT feature extraction with SVM classification and obtained 92% accuracy and [21] used DWT and PCA for feature extraction, fisher discriminant analysis (FDA) for classification and achieved 95% accuracy. ICA feature extraction [24] with linear discriminant analysis (LDA) classification obtained the higher accuracy of 99.3% on the FERET dataset compared [23] with 85% accuracy by using PCA feature extraction and LDA classification for gender prediction. On the one hand, Tapia and Perez [25] used various spatial scale feature fusions, which is selected using intensity, mutual information from shape, and histogram of LBP, where gender classification is performed by SVM and obtained 99.1% accuracy.…”

Section: Analysis Of Related Work For Facial Gender Recognitionmentioning

confidence: 99%

Real-Time Gender Recognition for Juvenile and Adult Faces

Gupta

Yesuf

Nain

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Facial gender recognition is a crucial research topic due to its comprehensive use cases, including a demographic gender survey, visitor profile identification, targeted advertisement, access control, security, and surveillance from CCTV. For these real-time applications, the face of a person can be oriented to any angle from the camera axis, and the person can be of any age group, including juveniles. A child’s face consists of immature craniofacial feature points in texture and edge compared to an adult face, making it very hard to recognize gender using the child’s face. Real-word faces captured in an unconstrained environment make the gender prediction system more complex to identify correctly due to orientation. These factors reduce the accuracy of the existing state-of-the-art models developed so far for real-time facial gender prediction. This paper presents the novelty of facial gender recognition for juveniles, adults, and unconstrained-oriented faces. The progressive calibration network (PCN) detects rotation-invariant faces in the proposed model. Then, a Gabor filter is applied to extract unique edge and texture features from the detected face. The Gabor filter is invariant to illumination and produces texture and edge features with redundant feature coefficients in large dimensions. Gabor has drawbacks such as redundancy and a large dimension resolved by the proposed meanDWT feature optimization method, which optimizes the system’s accuracy, the size of the model, and computational timing. The proposed feature engineering model is classified with different classifiers such as Naïve Bayes, Logistic Regression, SVM with linear, and RBF kernel. Its results are compared with the state-of-the-art techniques; detailed experimental analysis is presented and concluded to support the argument. We also present a review of approaches based on conventional and deep learning methods with their pros and cons for facial gender recognition on different datasets available for facial gender recognition.

show abstract

“…This is important in gender recognition because facial details carry discriminative and important information for gender classification. Many other gender recognition methods, such as [23], also use histogram equalization.…”

Section: A Pre-processingmentioning

confidence: 99%

“…2) Principal Component Analysis: As previously mentioned, because of high dimensionality of image data, PCA can be used to reduce the dimensions. For instance, PCA is used in [10], [21], [22], [23] for feature extraction and reduction. Inspired by them, PCA is used for the sake of dimension reduction.…”

Section: B First Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

A Fusion-based Gender Recognition Method Using Facial Images

Ghojogh

Shouraki

Mohammadzade

et al. 2018

Electrical Engineering (ICEE), Iranian Conference On

View full text Add to dashboard Cite

This paper proposes a fusion-based gender recognition method which uses facial images as input. Firstly, this paper utilizes pre-processing and a landmark detection method in order to find the important landmarks of faces. Thereafter, four different frameworks are proposed which are inspired by state-of-the-art gender recognition systems. The first framework extracts features using Local Binary Pattern (LBP) and Principal Component Analysis (PCA) and uses back propagation neural network. The second framework uses Gabor filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses lower part of faces as input and classifies them using kernel SVM. The fourth framework uses Linear Discriminant Analysis (LDA) in order to classify the side outline landmarks of faces. Finally, the four decisions of frameworks are fused using weighted voting. This paper takes advantage of both texture and geometrical information, the two dominant types of information in facial gender recognition. Experimental results show the power and effectiveness of the proposed method. This method obtains recognition rate of 94% for neutral faces of FEI face dataset, which is equal to state-of-the-art rate for this dataset.Index Terms-gender recognition, Gabor filter, local binary pattern, lower face, LDA, SVM, back propagation neural network, PCA.

show abstract

Gender classification using face recognition

Cited by 8 publications

References 7 publications

Review: Single attribute and multi attribute facial gender and age estimation

Review: Single attribute and multi attribute facial gender and age estimation

Real-Time Gender Recognition for Juvenile and Adult Faces

A Fusion-based Gender Recognition Method Using Facial Images

Contact Info

Product

Resources

About