The Hidden Sides of Names—Face Modeling with First Name Attributes

Abstract

This paper introduces the new idea of describing people using first names. We show that describing people in terms of similarity to a vector of possible first names is a powerful representation of facial appearance that can be used for a number of important applications, such as naming never-seen faces and building facial attribute classifiers. We build models for 100 common first names used in the US and for each pair, construct a pairwise first-name classifier. These classifiers are built using training images downloaded from the internet, with no additional user interaction. This gives our approach important advantages in building practical systems that do not require additional human intervention for data labeling. The classification scores from each pairwise name classifier can be used as a set of facial attributes to describe facial appearance. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. The name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the internet. We also demonstrate the powerful use of our name attributes for associating faces in images with names from caption, and the important application of unconstrained face verification.

Multi-scale score level fusion of local descriptors for gender classification in the wild

“…The reader must observe that the evaluation protocol is not exactly the same in most works. Indeed, it is not [8] 89.8 [9] 91.6 [36] 90.6 [19] 91.6 [11] 92.4 [31] Detected faces 2 86.4 [13] 90.4 [6] Adults 3 80.5 [23] Full dataset 87.1 [9] 90.8 [42] LFW Subset 4 94.8 [45] 98.0 [36] BEFIT 5 96.2 [15] 94.0 [40] Half dataset 6 98.0 [6] Full dataset 79.5 [41] 94.6 [28] 96.9…”

“…Chen and Gallagher [13] built a facial appearance representation based on the 100 most common names in USA. The voting of the top five names is used to assign a gender to a test image.…”

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

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“…Khan et al [72] suggested another algorithm, also called semantic pyramid gender and action recognition method, which addressed both gender and action recognition. Chen et al [43] proposed a multi modeling mechanism, which combined both the text and image information. Higher accuracy was reported as compared to SOA with the proposed model.…”

The Glass Closet: Perceptions of Homosexuality in Intercollegiate Sport

“…All the biographies coded for married consisted of a man and woman. This was determined based on traditional gender assigned names and pictures on webpages (Chen, Gallagher, & Girod, 2014). This was also used a coding method based on Rutgers athletic director Julie Herrmann official biography.…”