A study on face recognition techniques with age and gender classification

Kumar, Sandeep; Singh, Sukhwinder; Kumar, Jagdish

doi:10.1109/ccaa.2017.8229960

Cited by 31 publications

(9 citation statements)

References 47 publications

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“…Classification of classic gender recognition techniques. Inner images credits are for [26], [27] Recently, there were numerous attempts to use CNN to recognize gender [28]- [30] from "in the wild" images. However, the results did not cross an accuracy borderline of 88% in the best case [29].…”

Section: Gender Recognition From Facial Imagesmentioning

confidence: 99%

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet

Alghamdi

Taj-Eddin

2022

IJECE

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<p>Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and<br />CAS-PEAL-R1.</p>

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Section: Gender Recognition From Facial Imagesmentioning

confidence: 99%

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet

Alghamdi

Taj-Eddin

2022

IJECE

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“…In the last phasei.e. Face Recognition phase, matching is done with a help of a templates generated for enrollment in the FRS for verification/identification of a particular person [3]. Verification answers "Is the person who they say they are?…”

Section: Face Recognition Systemmentioning

confidence: 99%

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Saini¹,

Malhotra²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Among various types of biometric techniques it is the face recognition that gains huge popularity in the last few decades due to its vast applicationsand more user friendly nature.Face Recognition is the procedure of identification and verification of a person's identity using his face. Main steps involved in a FR System are Face Detection, Feature Extraction, Feature Selection and Recognition. Feature Extraction and Feature Selection are the two main phases to be focused on in order to get a good FR System. In this paper, we have carried out anextensive literature review on face recognition focusing on feature extraction and feature selection phases a bit more.

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“…After using median filter, histogram equalization is used to increase the pixel size to enhance the visualization as shown in Fig 5b. After then binarization is used to reduce the false connection between ridges by Fourier transformation and transform the original image in to binary image. Each pixel in the image contains xand y coordinates and several number of blocks in fingerprint image are specified as P and Q in horizontal and vertical direction, which are given in Equation (1) given below to get the frequency transform image (1) Where: i= 0, 1, 2, ..., 31…”

Section: Fig 1: Fingerprint Feature Set Extraction Processmentioning

confidence: 99%

“…Gray scale normalization eliminates the illumination effect on image and improves the recognition rate by image enhancement. After then PCA algorithm is used to extract the face feature set [31]. PCA algorithm is used to transforms the multidimensional data into low-dimensional data [32].…”

Section: Face Feature Extraction Processmentioning

confidence: 99%

A Robust Multimodel Biometric Crypto System

2019

IJRTE

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Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it makes system more robust and secure. In this paper, a robust multimodal biometric crypto system has been proposed, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems. Experimental results are shown with the help of MATLAB3. 2017b.

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A study on face recognition techniques with age and gender classification

Cited by 31 publications

References 47 publications

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Gender recognition from unconstrained selfie images: a convolutional neural network approach

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

A Robust Multimodel Biometric Crypto System

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