Ethnicity estimation with facial images

Hosoi, Satoshi; Takikawa, Erina; Kawade, Masato

doi:10.1109/afgr.2004.1301530

Cited by 74 publications

(48 citation statements)

References 4 publications

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“…Some recent works have looked at this task in the context of intensity images (Hosoi et al 2004;Lu and Jain 2004;Yang and Ai 2007) and 3-D range imagery (Lu et al 2006). The latter work suggests, similarly to the earlier case of gender, that 3-D information can be by itself superior to intensity information for the identification of ethnicity.…”

Section: Introductionmentioning

confidence: 99%

“…The latter work suggests, similarly to the earlier case of gender, that 3-D information can be by itself superior to intensity information for the identification of ethnicity. With the exception of Hosoi et al (2004), which considers African, Asian, and European classes, all of the cited works consider a binary classification problem: Asian versus non-Asian. This is not necessarily due to algorithmic limitations, but to a lack of standard datasets which contain significant representation from other classes.…”

Section: Introductionmentioning

confidence: 99%

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Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

et al. 2010

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While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art facerecognition algorithm. Once trained, our system is capable

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

et al. 2010

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“…But different from gender classification, ethnicity classification is much harder and sometimes even human can not have a very clear division for ethnicity in perception. In literature, G. Shakhnarovich et al [8] divided ethnicity into two categories: Asian and Non-Asian, while in [7] [18] [19] three categories with Mongoloid, Caucasoid and African were adopted, and in [17] four ethnic labels with Caucasian, South Asian, East Asian, and African are used. In this paper, we use three ethnic labels with Mongoloid, Caucasian and African.…”

Section: Gender Classification In a Multiethnic Environmentmentioning

confidence: 99%

Face Gender Classification on Consumer Images in a Multiethnic Environment

Gao

2009

Advances in Biometrics

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Abstract. In this paper, we target at face gender classification on consumer images in a multiethnic environment. The consumer images are much more challenging, since the faces captured in the real situation vary in pose, illumination and expression in a much larger extent than that captured in the constrained environments such as the case of snapshot images. To overcome the nonuniformity, a robust Active Shape Model (ASM) is used for face texture normalization. The probabilistic boosting tree approach is presented which achieves a more accurate classification boundary on consumer images. Besides that, we also take into consideration the ethnic factor in gender classification and prove that ethnicity specific gender classifiers could remarkably improve the gender classification accuracy in a multiethnic environment. Experiments show that our methods achieve better accuracy and robustness on consumer images in a multiethnic environment.

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“…An overall accuracy of 96.3% is achieved in their experiments. Hosoi et al [6] design ethnicity estimation method using Gabor wavelets transform and retinal sampling as features, and SVM as classifier. Three types of ethnic groups are classified: African, Asian, and European, and an overall approximately 94% accuracy is achieved.…”

Section: Introductionmentioning

confidence: 99%

Race recognition using local descriptors

Muhammad

Hussain

Alenezy

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a method for race recognition from face images using local descriptors. The proposed method uses two types of local descriptors: local binary pattern (LBP) and Weber local descriptors (WLD). First, LBP and WLD histograms are obtained separately from blocks of normalized face image. Kruskal-Wallis feature selection technique is applied to the histograms to select the significant bins for race recognition. Then the selected bins from the two histograms are concatenated block by block to produce the final feature set of the face image. Minimum city block distance is used as a classifier. The experiments are conducted using gray scale FERET images with five race groups. Experimental results show that the proposed method has superior race recognition accuracies for all the five race groups compared to LBP and WLD alone.

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Ethnicity estimation with facial images

Cited by 74 publications

References 4 publications

Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

Face Gender Classification on Consumer Images in a Multiethnic Environment

Race recognition using local descriptors

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