Gender Classification In Uncontrolled Settings Using Additive Logistic Models

Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively unconstrained. We localize faces using a standard sliding-window detector. We preprocess the facial region by convolving with Gabor filters at at four scales and four orientations. We sample these responses and concatenate them to form a feature vector. We develop a classifier based on an additive sum of non-linear functions of one-dimensional projections of the data. In particular we investigate arc tangent and weighted sums of Gaussians. We describe a training method based on increasing the binomial log likelihood. We demonstrate that our system on two databases and show that it performs well relative to the state of the art.

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: Binomial Log Likelihood, ICIP 2009, Image Processing, Standard Sliding-window Detector, Well-lit Frontal Images