Gender Recognition in the Wild with Small Sample Size - A Dictionary Learning Approach

D’Amelio, Alessandro; Cuculo, Vittorio; Buršić, Sathya

doi:10.1007/978-3-030-54994-7_12

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Deep Learning Based Facial Gender Recognition1

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2022

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“…By evaluating method on LFW, Adience and FERET datasets 95.98%, 90.43%, 99.28% accuracy achieved for gender classification. D Amelio et al [21] introduced a model for gender classification from real-world face images. In this model, features are extracted through VGG-Face Deep Convolutional Neural Network (DCNN).…”

Section: Deep Learning Based Facial Gender Recognitionmentioning

confidence: 99%

Review: Single attribute and multi attribute facial gender and age estimation

Gupta

Nain

2022

Multimed Tools Appl

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Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment.

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Section: Deep Learning Based Facial Gender Recognitionmentioning

confidence: 99%