On the Generalization Power of Face and Gait in Gender Recognition

On the Generalization Power of Face and Gait in Gender Recognition

Yu Guan, Xingjie Wei, Chang-Tsun Li
Copyright: © 2014 |Pages: 8
DOI: 10.4018/ijdcf.2014010101
(Individual Articles)
No Current Special Offers


Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.
Article Preview


Human gender recognition can be used in a wide range of real-world applications such as video surveillance. In terms of biometric traits, face and gait may be the most important modalities that can be used for gender classification (Ng et al., 2012). Although gender recognition are intensively studied by the previous literatures, most of them are based on a single dataset (Baluja & Yang, 2007; Li et al., 2008; Moghaddam et al., 2002; Shan et al., 2008; Wang et al., 2010). Unlike the human identification systems, gender recognition should be able to be performed across different datasets in real-world scenarios (Ng et al., 2012). Each dataset has its own database bias due to its own unique data collection environments, yet in the context of face/gait-based gender recognition, most of the previous works simply neglect this issue. Although several popular methods like SVM (Moghaddam et al., 2002; Li et al., 2008), AdaBoost (Baluja & Yang,2007; Wang et al., 2010), PCA+LDA (Shan et al., 2008; Chang et al., 2009) can yield high performance on the same dataset (referred to as intra-dataset), they are seldom evaluated in a cross-dataset manner. In this work, we test these algorithms to see whether they are robust enough against the bias from different datasets. Figure 1 demonstrates several face images from 5 different face datasets while Figure 2 provides some Gait Energy Images (GEI, i.e., average gait silhouette over a gait cycle (Han & Bhanu, 2006)) from 2 different gait datasets. Can you tell the bias pattern for each group of face/gait images in Figures 1 and 2.

Figure 1.

Cropped images from the face datasets: (a) AR; (b) FERET; (c) FRGC; (d) LFW; and (e) TFWM

Figure 2.

GEI samples: (a) female samples from CASIA-B dataset; (b) male samples from CASIA-B dataset; (c) female samples from USF dataset; (d) male samples from USF dataset


Experimental Setup And Results Analysis

In the previous works, high performance can be achieved when conventional machine learning methods like SVM, AdaBoost, PCA+LDA are used for face/gait-based gender recognition. However, gender is a cue across all datasets and should be independent of specific face/gait dataset. Since each dataset has its own bias (Torralba & Efros, 2011) due to its own unique data collection environments, in this work by testing several popular algorithms in a cross-dataset manner, we aim to evaluate the generalization power of these methods, which are important for practical applications. Correct Classification Rate (CCR) is used to measure the performance.

  • IGI Global’s Seventh Annual Excellence in Research Journal Awards
    IGI Global’s Seventh Annual Excellence in Research Journal AwardsHonoring outstanding scholarship and innovative research within IGI Global's prestigious journal collection, the Seventh Annual Excellence in Research Journal Awards brings attention to the scholars behind the best work from the 2014 copyright year.

Complete Article List

Search this Journal:
Volume 16: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 15: 1 Issue (2023)
Volume 14: 3 Issues (2022)
Volume 13: 6 Issues (2021)
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing