Husky Masquerade

Award for Best Demonstration . Abstract Face detection is the process where machines identify faces within an image or visual field. Face detection is used in analyzing emotions, and even in social networking applications, such as Snapchat. The underlying mechanism of face detection is to locate key landmarks on a person’s face. The goal is to detect faces using a webcam, find the facial landmarks of the detected faces, and overlay customized images relative to the facial landmarks. Machines need to be taught to detect faces. It is crucial to teach the program to identify the different types of jawlines. The program is provided a data model which specifies different combinations of facial landmark positions. The machine is then able to compare the objects seen in its visual field to the data model. Once the machine recognizes the landmarks, it places image overlays across the visual display or mutates the visual display. These images can be stationary across the visual field, or mobile relative to the facial landmarks. The programs built for this project are capable of detecting faces using a webcam, finding the facial landmarks of the detected faces, and overlaying customized images relative to the facial landmarks. Before the programs were created, existing software, toolboxes, and libraries were surveyed. The project’s programs closely resemble Snapchat and Instagram filters. The limitation of the programs built for this project is that they have not been optimized for smartphones; the programs work only on a laptop or a smaller computer, such as the Raspberry Pi. Hence, the programs are not widely accessible to the general public

An effective genetic algorithm for the minimum-label spanning tree problem

Given a connected, undirected graph G with labeled edges, the minimum-label spanning tree problem seeks a spanning tree on G to whose edges are attached the smallest possible number of labels. A greedy heuristic for this NP-hard prob-lem greedily chooses labels so as to reduce the number of components in the subgraphs they induce as quickly as possi-ble. A genetic algorithm for the problem encodes candidate solutions as permutations of the labels; an initial segment of such a chromosome lists the labels that appear on the edges in the chromosome's tree. Three versions of the GA apply generic or heuristic crossover and mutation operators and a local search step. In tests on 27 randomly-generated instances of the minimum-label spanning tree problem, ver-sions of the GA that apply generic operators, with and with-out the local search step, perform less well than the greedy heuristic, but a version that applies the local search step and operators tailored to the problem returns solutions that require on average 10 % fewer labels than the heuristic's

Comparing Decoding Algorithms In A Weight-Coded GA For TSP

A novel coding of candidate solutions in genetic algorithms for combinatorial problems associates numerical weights with elements of the target problem instance. The solution a chromosome of weights represents is made explicit by a heuristic algorithm for the problem whose actions the weights influence. The choice of the heuristic---called the decoding algorithm---is a crucial one in a genetic algorithm that employs such a coding. This paper describes a weighted coding of tours in a genetic algorithm for the traveling salesman problem and an investigation of nine heuristics for TSP as decoding algorithms in that GA. Two greedy heuristics performed poorly, but heuristics that build tours by insertion--- adding each new city so as to increase the tour length the least---did better. Several showed excellent performance on TSP instances of moderate size. The results indicate both the importance of the decoding algorithm in a GA that uses a weighted coding and the potential of such codings..