Journal of Naval Sciences and Engineering

14

Authors: Furkan GÜRPINAR, Heysem KAYA, Sadaf AFSHAR, Hamdi DİBEKLİOĞLU, Albert Ali SALAH

This paper presents a framework for estimating the apparent age of a subject from their face image. To learn the age estimation model, we used a set of visual descriptors and their feature-level fusion to obtain a single feature vector for apparent age. For model learning, we used regression with Extreme Learning Machines (ELM) with Gaussian (RBF) kernels. We tested the proposed system with k-fold cross-validation on the dataset provided by ChaLearn Looking at People 2015 - Apparent Age Estimation challenge. By combining visual descriptors from multiple grids, we obtained a Mean Absolute Error (MAE) of 5.20.

19

Authors: Muhammed Miraç SÜZGÜN, Hilal ÖZDEMİR, Necati Cihan CAMGÖZ, Ahmet Alp KINDIROĞLU, Doğaç BAŞARAN, Cengiz TOGAY, Lale AKARUN

Sign language is the natural medium of communication for the Deaf community. In this study, we have developed an interactive communication interface for hospitals, HospiSign, using computer vision based sign language recognition methods. The objective of this paper is to review sign language based Human-Computer Interaction applications and to introduce HospiSign in this context. HospiSign is designed to meet deaf people at the information desk of a hospital and to assist them in their visit. The interface guides the deaf visitors to answer certain questions and express intention of their visit, in sign language, without the need of a translator. The system consists of a computer, a touch display to visualize the interface, and Microsoft Kinect v2 sensor to capture the users’ sign responses. HospiSign recognizes isolated signs in a structured activity diagram using Dynamic Time Warping based classifiers. In order to evaluate the developed interface, we performed usability tests and deduced that the system was able to assist its users in real time with high accuracy.