Refik Can Mallı

Blaž Meden, Refik Can Mallı, Sebastjan Fabijan et al.

Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelization were replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and at the same time aim at retaining certain characteristics of the data even after deidentification. The latter aspect is particularly important, as it allows to exploit the deidentified data in applications for which identity information is irrelevant. In this work we present a novel face deidentification pipeline, which ensures anonymity by synthesizing artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or video, while preserving non-identity-related aspects of the data and consequently enabling data utilization. Since generative networks are very adaptive and can utilize a diverse set of parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race, etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of our approach, we perform experiments using automated recognition tools and human annotators. Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.

Refik Can Malli, Mehmet Aygun, Hazim Kemal Ekenel

In this paper, we address the problem of apparent age estimation. Different from estimating the real age of individuals, in which each face image has a single age label, in this problem, face images have multiple age labels, corresponding to the ages perceived by the annotators, when they look at these images. This provides an intriguing computer vision problem, since in generic image or object classification tasks, it is typical to have a single ground truth label per class. To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range. Using these age groups and their age-shifted groupings, we have trained an ensemble of deep learning models. Before feeding an input face image to a deep learning model, five facial landmark points are detected and used for 2-D alignment. We have employed and fine tuned convolutional neural networks (CNNs) that are based on VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The outputs of these deep learning models are then combined to produce the final estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP 2016 challenge test set [5].