Face Analysis: State of the Art and Ethical Challenges

In face analysis, the task is to identify a subject appearing in an image as a unique individual and to extract facial attributes like age, gender, and expressions from the face image. Over the last years, we have witnessed tremendous improvements in face analysis algorithms developed by the industry and by academia as well. Some applications, that might have been considered science fiction in the past, have become reality now. We can observe that nowadays tools are far from perfect, however, they can deal with very challenging images such as pictures taken in an unconstrained environment. In this paper, we show how easy is to build very effective applications with open source tools. For instance, it is possible to analyze the facial expressions of a public figure and his/her interactions in the last 24 h by processing images from Twitter given a hashtag. Obviously, the same analysis can be performed using images from a surveillance camera or from a family photo album. The recognition rate is now comparable to human vision, but computer vision can process thousands of images in a couple of hours. For these applications, it is not necessary to train complex deep learning networks, because they are already trained and available in public repositories. In our work, we show that anyone with certain computer skills can use (or misuse) this technology. The increased performance of facial analysis and its easy implementation have enormous potential for good, and –unfortunately– for ill too. For these reasons, we believe that our community should discuss the scope and limitations of this technology in terms of ethical issues such as definition of good practices, standards, and restrictions when using and teaching facial analysis.