Gender Classification Based on Boosting Local Binary Pattern

Sun, Ning; Zheng, Wenming; Sun, Changyin; Zou, Cairong; Zhao, Li

doi:10.1007/11760023_29

Cited by 88 publications

(51 citation statements)

References 9 publications

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“…Experiments were performed to predict age, gender and ethnicity from face images. A similar approach was proposed in [25]. Other local descriptors have also been adopted for gender classification.…”

Section: Related Workmentioning

confidence: 99%

“…A systematic overview on the topic of gender classification from face images can be found in [17]. Among all the descriptors that encode gender information such as LBP [25], SIFT [26] and HOG [6], the LBP has shown good discrimination capability while maintaining simplicity [17]. To establish a base-line for appearancebased methods, we use LBP in combination with SVM to predict gender from facial images in this work.…”

Section: Related Workmentioning

confidence: 99%

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Can facial metrology predict gender?

Cao

Chen

Piccirilli

et al. 2011

2011 International Joint Conference on Biometrics (IJCB)

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We investigate the question of whether facial metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. Here, metrological features are defined in terms of specially normalized angle and distance measures and computed based on given landmarks on facial images. The performance of the proposed metrologybased method is compared with that of a state-of-the-art appearance-based method for gender classification. Results are reported on two standard face databases, namely, MUCT and XM2VTS containing 276 and 295 images, respectively. The performance of the metrology-based approach was slightly lower than that of the appearancebased method by only about 3.8% for the MUCT database and about 5.7% for the XM2VTS database.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Can facial metrology predict gender?

Cao

Chen

Piccirilli

et al. 2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

show abstract

“…Experiments were performed to predict the age, gender, and ethnicity information from face images. Similar work was presented in Sun et al (2006), where LBP features and Adaboost classifier were combined to achieve better performance. Other local-based descriptors have also been adopted in the work of gender classification.…”

Section: Soft Biometrics For Surveillance: An Overviewmentioning

confidence: 97%

“…Feature extraction methods include the use of raw pixel face images (Moghaddam and Yang, 2002;Baluja and Rowley, 2007), Principle Component Analysis (PCA) (Balci and Atalay, 2002), Linear Discriminant Analysis (LDA) (Bekios Calfa et al, 2011), Independent Component Analysis (ICA) (Jain and Huang, 2004), LBP (Yang and Ai, 2007;Shan, 2010;Sun et al, 2006;Chen and Ross, 2011), and metrology . Some feature selection algorithms (Khan et al, 2005) have also been used to select gender specific features.…”

Section: Soft Biometrics For Surveillance: An Overviewmentioning

confidence: 99%

Soft Biometrics for Surveillance: An Overview

Reid

Samangooei

Chen

et al. 2013

Handbook of Statistics - Machine Learning: Theory and Applications

110

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sp005Biometrics is the science of automatically recognizing people based on physical or behavioral characteristics such as face, fingerprint, iris, hand, voice, gait, and signature. More recently, the use of soft biometric traits has been proposed to improve the performance of traditional biometric systems and allow identification based on human descriptions. Soft biometric traits include characteristics such as height, weight, body geometry, scars, marks, and tattoos (SMT), gender, etc. These traits offer several advantages over traditional biometric techniques. Soft biometric traits can be typically described using human understandable labels and measurements, allowing for retrieval and recognition solely based on verbal descriptions. Unlike many primary biometric traits, soft biometrics can be obtained at a distance without subject cooperation and from low quality video footage, making them ideal for use in surveillance applications. This chapter will introduce the current state of the art in the emerging field of soft biometrics.

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“…LBP and its variants can be uniform and/or rotation invariant [21] and have been extensively exploited in many applications, for instance, facial image analysis, including face detection [22][23][24][25], face recognition and facial expression analysis [26][27][28][29][30][31][32][33][34]; demographic (gender, race, age, etc.) classification [35][36][37][38]; moving object detection [39], etc. The major reasons behind the popularity of LBP based methods are their computational simplicity, robustness against monotonic illumination variation and better performance in several areas.…”

Section: Texture Based Classificationmentioning

confidence: 99%

An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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Gender Classification Based on Boosting Local Binary Pattern

Cited by 88 publications

References 9 publications

Can facial metrology predict gender?

Can facial metrology predict gender?

Soft Biometrics for Surveillance: An Overview

An Automated System for Garment Texture Design Class Identification

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