Human Age Estimation Using Ranking SVM

Cao, Dong; Lei, Zhen; Zhang, Zhiwei; Feng, Jun; Li, Stan Z.

doi:10.1007/978-3-642-35136-5_39

Cited by 24 publications

(12 citation statements)

References 18 publications

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“…Using of kernel functions are key success SVM in classification when kernel functions get high capacity control to optimize margin value [30]. Performing process locally, SVM operate process classification locally adjustment will able to pre result in a score the features of single company, differ input variables with the one of training sample to show in ratios values [29]. Some limitations of SVM in modelling could be stated are; selection Kernel Functions, limitation of SVM in the choice of the kernel to maximum the margin is well regularized, so the infinite dimensions do not give spoil the results [30,31].…”

Section: Discussionmentioning

confidence: 99%

Classification of gender using neural network and Support Vector Machine from bone morphology properties of monkey

Sahadun

Haron

Kadir

2013

2013 IEEE Conference on Open Systems (ICOS)

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Forensic anthropologists are often asked to evaluate partial skeletal remains or severely damaged. The purpose of this research is comparing the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for gender classification. The performance of both models in classifying the gender data is compared and validated using the Ryan and Shaw Dataset (RSD) in terms of accuracy, sensitivity and specificity percentage. These measurements were taken on 226 femurs (126 females and 100 males) and 216 humerus (126 females and 90 males) of old world monkey primates. The comparison result shows that the ANN classifier outperforms SVM classifier for correctly classifying the bone morphology properties in gender data in term of accuracy and specificity percentage value; 71 % and 56% respectively. The conclusion of Artificial Neural Network (ANN) is a powerful classification model that improves the accuracy rate of gender classification models for skeletal remains.

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Section: Discussionmentioning

confidence: 99%

Classification of gender using neural network and Support Vector Machine from bone morphology properties of monkey

Sahadun

Haron

Kadir

2013

2013 IEEE Conference on Open Systems (ICOS)

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“…Classification of a face image is performed by considering the likelihood of it belonging to each of these subspaces. Support vector machines were used for classifying age in [14], [31], and more recently [32].…”

Section: Previous Workmentioning

confidence: 99%

Age and Gender Estimation of Unfiltered Faces

Eidinger¹,

Enbar²,

Hassner

2014

IEEE Trans.Inform.Forensic Secur.

720

544

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This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of datawe offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid overfitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.Index Terms-Face recognition, identification of persons, support vector machines, neural networks. 1556-6013

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“…The initially rank relationship of ages is learned from various face images. Then the human age is estimated based on the rank relationship and the age information of a reference set [5]. Khoa Luu et al used a novel age estimation technique that combines AAMs and SVMs to improve the accuracy of age estimation over the current state-to-the art technique [6].…”

Section: Related Workmentioning

confidence: 99%

Age Group Estimation by Combining Texture and Fractal Analysis

Bansode¹,

Sinha²

2016

IJCA

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In this paper the age group estimation is presented based on combination of texture and fractal dimension features. The age of the human is used as one of the important key parameter for computer vision applications. The fractal dimension of the face image and the texture analysis is used to classify the age of the person into the three different groups such as child(10-20), young(21-50) and old(51 and above. The proposed approach of combing the fractal and texture features shows an effective estimation of the age group. The facial age groups are estimated with 90% average accuracy.

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Human Age Estimation Using Ranking SVM

Cited by 24 publications

References 18 publications

Classification of gender using neural network and Support Vector Machine from bone morphology properties of monkey

Classification of gender using neural network and Support Vector Machine from bone morphology properties of monkey

Age and Gender Estimation of Unfiltered Faces

Age Group Estimation by Combining Texture and Fractal Analysis

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