Gender Recognition Using Fusion of Spatial and Temporal Features

Sett, Kaushik; Das, Pritam; Ghosh, Prosun; Ghosh, Surya K.; Dey, Aritra; Saha, Neelanjan; Biswas, Suparna

doi:10.1007/978-3-319-07353-8_13

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Comparisons with the existing methods are summarized in Table III. In Table III proposed method is compared with [26], [27] and [28]. The proposed method provide better recognition rate compared to [26], [27] and [28].…”

Section: Resultsmentioning

confidence: 99%

Compressive Sensing based Gender Recognition

Sil¹,

SANTI²,

BISWAS³

2018

Sixth International Conference on Advances in Computing Communication and Information Technology CCIT 2018

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This paper explores an integration of compressive sensing, curvelet transform, and Principal Component Analysis to develop a robust gender recognition method from face images. Compressive measurements of face images leading to a significant reduction in feature space. Here curvelet transform has been used to represent the face images with prominent edges, curvatures, boundaries and to offer sparse representation to apply compressive measurements on detailed subband. To extract the feature vector, Principal Component Analysis is applied on the reconstructed detailed subband. Performance of the proposed method is evaluated by employing different classifiers. The proposed method efficiently handles the effect of Gaussian noise maintaining high accuracy on gender recognition. Extensive experiments on FERET database, is conducted to substantiate our claim.

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Section: Resultsmentioning

confidence: 99%

Compressive Sensing based Gender Recognition

Sil¹,

SANTI²,

BISWAS³

2018

Sixth International Conference on Advances in Computing Communication and Information Technology CCIT 2018

Self Cite

View full text Add to dashboard Cite

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“…Features of Stanford and FG-Net datasets are described in Table 2. Moreover, for FERET database, we have used 200 test images to evaluate the proposed approach as suggested in [14].…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, the results of applying the gender recognition methods Male Female Male Female FG-Net 80 × 80 800 320 320 80 80 Stanford 60 × 60 400 160 160 40 to FERET database are shown in Table 5. The proposed method is placed in the middle of the table with a slight difference with the methods of Leng and Wang [8] and Biswas and Sil [14]. Another important fact about the proposed gender recognition approach is its relatively low computational time as it takes only a few seconds for detecting the gender of a face illustrated in an image.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Gender classification based on fuzzy clustering and principal component analysis

Hassanpour

Zehtabian

Nazari

et al. 2016

IET Computer Vision

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Gender classification is one of the most challenging problems in computer vision. Facial gender detection of neonates and children is also known as a highly demanding issue for human observers. This study proposes a novel gender classification method using frontal facial images of people. The proposed approach employs principal component analysis (PCA) and fuzzy clustering technique, respectively, for feature extraction and classification steps. In other words, PCA is applied to extract the most appropriate features from images as well as reducing the dimensionality of data. The extracted features are then used to assign the new images to appropriate classes – male or female – based on fuzzy clustering. The computational time and accuracy of the proposed method are examined together and the prominence of the proposed approach compared to most of the other well‐known competing methods is proved, especially for younger faces. Experimental results indicate the considerable classification accuracies which have been acquired for FG‐Net, Stanford and FERET databases. Meanwhile, since the proposed algorithm is relatively straightforward, its computational time is reasonable and often less than the other state‐of‐the‐art gender classification methods.

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