The analysis of facial beauty: an emerging area of research in pattern analysis

Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical application

Models and algorithms for energy-efficient scheduling with immediate start of jobs

We study a scheduling model with speed scaling for machines and the immediate start requirement for jobs. Speed scaling improves the system performance, but incurs the energy cost. The immediate start condition implies that each job should be started exactly at its release time. Such a condition is typical for modern Cloud computing systems with abundant resources. We consider two cost functions, one that represents the quality of service and the other that corresponds to the cost of running. We demonstrate that the basic scheduling model to minimize the aggregated cost function with n jobs is solvable in O(nlogn) time in the single-machine case and in O(n²m) time in the case of m parallel machines. We also address additional features, e.g., the cost of job rejection or the cost of initiating a machine. In the case of a single machine, we present algorithms for minimizing one of the cost functions subject to an upper bound on the value of the other, as well as for finding a Pareto-optimal solution

A unified approach for scheduling with convex resource consumption functions using positional penalties

We provide a unified model for solving single machine scheduling problems with controllable processing times in polynomial time using positional penalties. We show how this unified model can be useful in solving three different groups of scheduling problems. The first group includes four different due date assignment problems to minimize an objective function which includes costs for earliness, tardiness, due date assignment, makespan and total resource consumption. The second group includes three different due date assignment problems to minimize an objective function which includes the weighted number of tardy jobs, due date assignment costs, makespan and total resource consumption costs. The third group includes various scheduling problems which do not involve due date assignment decisions. We show that each of the problems from the first and the third groups can be reduced to a special case of our unified model and thus can be solved in O(n3) time. Furthermore, we show how the unified model can be used repeatedly as a subroutine to solve all problems from the second group in O(n4) time. In addition, we also show that faster algorithms exist for several special cases.Single machine scheduling Controllable processing times Resource allocation Due date assignment Positional penalties Polynomial-time algorithm

Data-driven enhancement of facial attractiveness

When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal (or the attractiveness) of human faces in frontal photographs (portraits), while maintaining close similarity with the original. The key component in our approach is an automatic facial attractiveness engine trained on datasets of faces with accompanying facial attractiveness ratings collected from groups of human raters. Given a new face, we extract a set of distances between a variety of facial feature locations, which define a point in a high-dimensional “face space”. We then search the face space for a nearby point with a higher predicted attractiveness rating. Once such a point is found, the corresponding facial distances are embedded in the plane and serve as a target to define a 2D warp field which maps the original facial features to their adjusted locations. The effectiveness of our technique was experimentally validated by independent rating experiments, which indicate that it is indeed capable of increasing the facial attractiveness of most portraits that we have experimented with. Keywords: warping

Digital face beautification

Beauty has fascinated human beings from the very dawn of mankind. In particular, the beauty of the human face has inspired countless artists, poets, and philosophers. Several psychological studies indicate that facial attractiveness is a universal notion, transcendin

Video Operations in the Gradient Domain

Fusion of image sequences is a fundamental operation in numerous video applications and usually consists of segmentation, matting and compositing. We present a unified framework for performing these operations on video in the gradient domain. Our approach consists of 3D graph cut computation followed by reconstruction of a new 3D vector field by solving the Poisson equation. We introduce new methods for fusing video smoothly and separating foreground elements from a video background for compositing by defining smooth and sharp transition constraints on a gradient video compositing equation. We demonstrate the applicability of smooth video transitions by fusing pairs for video mosaics, video folding, and video texture synthesis, and demonstrate the applicability of sharp video transitions by video segmentation, video trimap extraction and 3D compositing into a new sequence. Our results demonstrate that our method maintains coherence of the video matte and composite, and avoids temporal artifacts. tion