Fast gender classification

Ozbudak, Ozlem; Tükel, Mehmet; Şeker, Serhat

doi:10.1109/iccic.2010.5705804

Cited by 15 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Meskipun telah banyak dilakukan penelitian tentang pengenalan wajah, hanya sedikit yang melakukan penelitian tentang pengenalan jenis kelamin menggunakan citra wajah [4].…”

Section: Pendahuluanunclassified

Pembuatan Perangkat Lunak Untuk Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah

Anggriawan

Hikmah²

2021

INFOS Journal

View full text Add to dashboard Cite

In this study, the 2DPCA feature extraction method and SOM classification were used to classify human sex based on facial images. The trial was conducted on 100 facial images consisting of 50 male facial images and 50 female facial images. The 100 facial image data used were divided into 70 training data and 30 test data. Based on the test results, the best accuracy obtained is 86.67% using a start learning rate of 0.6 and a maximum iteration of 10000. level of accuracy, where a larger maximum iteration value results in a higher level of accuracy.

show abstract

“…Meskipun telah banyak dilakukan penelitian tentang pengenalan wajah, hanya sedikit yang melakukan penelitian tentang pengenalan jenis kelamin menggunakan citra wajah [4].…”

Section: Pendahuluanunclassified

Pembuatan Perangkat Lunak Untuk Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah

Anggriawan

Hikmah²

2021

INFOS Journal

View full text Add to dashboard Cite

show abstract

“…Ozbudak et al [22] have used Discrete Wavelet transform on facial images, followed by PCA for reducing image dimension and finally Fisher Linear Discriminant has been applied to get an accuracy around 93.4%. In [6], the authors have tried out different feature extraction techniques such as the Discrete Cosine Transform (DCT), Wavelet Transform, Gray-Level Co-occurrence matrix (GLCM).…”

Section: Related Workmentioning

confidence: 99%

Recognizing gender from human facial regions using genetic algorithm

et al. 2018

View full text Add to dashboard Cite

Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender specific information from facial images. However, analyzing different facial regions and their fusion help in deciding the gender of a person from facial images. In this paper, we propose a new approach to identify gender from frontal facial images that is robust to background, illumination, intensity, and facial expression. In our framework, first the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al. The model provides 49 facial landmark points covering different regions of the face, e.g. forehead, left eye, right eye, lips. Next, a face image is segmented into facial regions using landmark points and features are extracted from each region. The Compass LBP feature, a variant of LBP feature, has been used in our framework to obtain discriminative gender specific information. Following this, a Support Vector Machine based classifier has been used to compute the probability scores from each facial region. Finally, the classification scores obtained from individual regions are combined with a genetic algorithm based learning to improve 2 Avirup Bhattacharyya et al.the overall classification accuracy. The experiments have been performed on popular face image datasets such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS and CUFSF. Through experiments, we have observed that, the proposed method outperforms existing approaches.The face of a human being reveals useful information about his/her identity, ethnicity, age, gender, expression, and emotion. Gender identification from facial images plays an important role in various computer vision based applications. It is possible for human beings to correctly determine gender by looking at the facial appearance. The advancements of computer vision technologies have inspired the researchers to design systems capable of performing similar functions [15,26,30]. Human computer interaction system, surveillance system, content based indexing and searching, biometric, and targeted advertising are some of the areas where it has been widely used. In a typical face recognition system, researchers face a number of challenges due to variations in pose, illumination, occlusion, expression, and lower resolution that essentially hamper a system's performance. Hence, the challenge is to design a system that is fairly invariant to changes in illumination, pose, resolution, and facial expression. These challenges have been effectively dealt with by the authors in [20,27,36]. However, gender recognition in such varying conditions has not been fully addressed yet. This paper deals with gender recognition of frontal face images with variations in illumination, pose, facial expression and image resolution.Feature dimension and computational complexity are important aspects that a typical gen...

show abstract

“…Different techniques involve LBP [4], Support Vector Machines (SVM) [5], Weber Local Descriptor (WLD) [6], Discrete Wavelet Transform (DWT) [7], Principal Component Analysis (PCA) [8] and Interlaced Derivative Pattern (IDP) [9]. Some techniques also involve the combination of two techniques to improve the results.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Approach for Gender Recognition from Face Images with Embedded Bandlets

Shabbir

Khan

Irtaza

et al. 2015

Artificial Intelligence and Soft Computing

View full text Add to dashboard Cite

Abstract. In this paper we have proposed a gender recognition system through facial images. We have used three different techniques that involve Bandlet Trans-form (a multi-resolution technique), LBP (Local Binary Pattern) and mean to create the feature vectors of the images. To classify the images for gender, we have used fuzzy c mean clustering. SUMS and FERET databases were used for testing. Experimental results have shown that the maximum average accuracy was achieved using SUMS, 97.1% has been achieved using Band-lets and mean technique, Bandlets and whole image LBP has shown 85.13% and Bandlets with blocked based LBP has shown 87.02% average accuracy.

show abstract

Fast gender classification

Cited by 15 publications

References 15 publications

Pembuatan Perangkat Lunak Untuk Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah

Pembuatan Perangkat Lunak Untuk Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah

Recognizing gender from human facial regions using genetic algorithm

A Fuzzy Logic Approach for Gender Recognition from Face Images with Embedded Bandlets

Contact Info

Product

Resources

About