Face Detection by Direct Convexity Estimation

. We suggest a novel attentional mechanism for detection of smooth convex and concave objects based on direct processing of intensity values. The operator detects the regions of the eyes and hair in a facial image, and thus allows us to infer the face location and scale. Our operator is robust to variations in illumination, scale, and face orientation. Invariance to a large family of functions, serving for lighting improvement in images, is proved. An extensive comparison with edgebased methods is delineated. 1 Introduction Edge detection was, so far, the core of most state of the art techniques for attentional mechanisms as well as face detection (see [4], [6]). This excludes some recent works which utilize neural networks ([9],[7]), color histograms ([2],[8]), or shape statistics ([1],[5]) for face detection. Though one cannot disregard their advantages, edge maps sustain severe flaws such as: sensitivity to changes in illumination, strong effect of surrounding objects, and inabilit..

Abstract Face Detection by Direct Convexity Estimation

We suggest a novel attentional mechanism for detection of smooth convex and concave objects based on direct processing of intensity values. The operator detects the regions of the eyes and hair in a facial image, and thus allows us to infer the face location and scale. Our operator is robust to variations in illumination, scale, and face orientation. Invariance to a large family of functions, serving for lighting improvement in images, is proved. An extensive comparison with edge-based methods is delineated. Key words: Face Detection. Convexity. Gradient Argument.

Competition Law as Collective Bargaining Law

As scholars and policymakers around the world seek a systematic approach to the question of \u27gig work,\u27 one of its regulatory dimensions – the intersection of labor and competition law – points toward a deeper reconceptualization of the conventional legal and economic categories typically brought to bear upon it. A comparative approach to the question of gig work further reveals the variety and contingency of background assumptions that are often overlooked in the context of domestic policy debates. By combining a detailed comparative doctrinal survey of the regulation of non-employee workers in domestic competition law systems with a set of essays reframing the underlying questions raised – in terms of international legal frameworks, freedom of association norms, alternative approaches to law and economics, and more – The Cambridge Handbook of Labor in Competition Law moves the debates over the fissured workplace and the labor – competition law intersection forward in novel ways.https://scholarship.law.ua.edu/fac_bookchapter/1002/thumbnail.jp