Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces

By Erno Mäkinen, Roope Raisamo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

392 citations recorded by the source corpus.

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Unequal Representation and Gender Stereotypes in Image Search Results for Occupations (2015, background): “While state-of-the art classifiers perform well under certain circumstances, they have historically focused on straight-on images of a single face, typically without visual occlusions, uneven lighting, or complicated backgrounds [25].”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “C. Automatic Quality Assessment of Extracted Soft Biometric Traits On a similar note, another challenge is to reliably extract these traits.”
Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems (2018, methodology): “Recently, there has been an increasing corpus of research discussing AGR and transgender individuals [26,30,48].”
Age and gender classification using convolutional neural networks (2015, methodology): “Before describing the proposed method we brieﬂy review related methods for age and gender classiﬁcation and provide a cursory overview of deep convolutional networks.”
Learning local binary patterns for gender classification on real-world face images (2012, background): “Mäkinen and Raisamo (2008) systematically evaluated different face alignment and gender recognition methods on the FERET database.”