Gender Detection using Facial Features with Support Vector Machine

Hiremath, Jayaprada S.; Hiremath, Shantala S.; Kumar, Sujith; Chincholi, Manjunath S.; Patil, Shantakumar B.; Hiremath, Mrutyunjaya S.

doi:10.1109/icmnwc56175.2022.10031856

Cited by 3 publications

(1 citation statement)

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“…A SIFT algorithm known as SURF is used to locate objects of interest in images that are independent of scale and orientation. The SURF algorithm is a quicker and more reliable variant of the SIFT method [40]. While using SURF, an image's stable key points are first found, and then their descriptors are computed.…”

Section: Speeded Up Robust Features (Surf)mentioning

confidence: 99%

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

Al-Dujaili

Alrikabi

abed

et al. 2023

Int. J. Interact. Mob. Technol.

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In the realm of robotics and interactive systems, gender recognition is a crucial problem. Considering the several uses it has in security, web search, human-computer interactions, etc., gender recognition from facial photos has garnered a lot of attention. The need to use and enhance gender recognition techniques is felt more strongly today due to a significant development in the design of facial recognition systems. Relatively speaking to other approaches, the progress gained in this area thus far is not exceptional. Thus, a novel method has been adopted in this study to improve accuracy in comparison to earlier research. To create the best rate of accuracy and efficiency in the suggested method of this research, we choose a minimal set of characteristics. Testing on the FERET and UTK-Face datasets reveals that our suggested algorithm has a lower degree of inaccuracy. In this article, the input image of the person's face is pre-processed to extract the right features from the face once the person's face has been recognized. Gender separation is achieved using Multi-class Support Vector Machine (SVM) Classifiers after features from normalized images have been extracted using Histogram Oriented Gradient (HOG), Gabor Filters, and Speeded Up Robust Features (SURF), as well as their combination to select the most appropriate feature from them as input for gender classification. As a feature reduction feature, the Principal Component Analysis (PCA) algorithm is also employed. Using the proposed approach, 98.75% gender recognition precision has been accomplished on the FERET database and a runtime performance of 0.4 Sec. on the UTK-Face database, 97.43% gender recognition accuracy has been accomplished and a runtime performance of 0.5 Sec.

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Section: Speeded Up Robust Features (Surf)mentioning

confidence: 99%