Ali Sharifara

A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection

Face detection is an interesting area in research application of computer vision and pattern recognition, especially during the past several years. It is also plays a vital role in surveillance systems which is the first steps in face recognition systems. The high degree of variation in the appearance of human faces causes the face detection as a complex problem in computer vision. The face detection systems aimed to decrease false positive rate and increase the accuracy of detecting face especially in complex background images. The main aim of this paper is to present an up-to-date review of face detection methods including feature-based, appearance-based, knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. We also conclude this paper with some discussions on how the work can be taken further.

An Interactive Robot-based Vocational Assessment Game using Lego Assembly

Lego construction task paradigms are utilized in order to develop logico-mathematical abilities through visuospatial memory. This study aims to assess the relationship between cognition and performance in a simulated industrial environment by employing humanoid robots to assess the stated metrics. This system proposes to develop a smart vocational assessment and intervention service system that assesses a workers needs for training and rehabilitation in an experimental setup that simulates a factory. The proposed approach collects and analyzes multi-sensing data and recommends personalized interventions that can improve the performance of and individual worker. In our implementation, Aldebarans NAO robot gradually learns the features and thresholds needed to construct a decision tree that gradually learns the expected Lego model by interacting with the user. The results from 615 test samples show that the NAO robot is able to correctly identify the Lego blocks configuration assembled by the user with an accuracy 81% of the time. Finally, we discuss the limitations of the proposed solution and we suggest future contributions that can overpass these limitations and boost the accuracy of our proposed solution.

A Robot-Based Cognitive Assessment Model Based on Visual Working Memory and Attention Level

Vocational assessment is the process of identifying and assessing an individual’s level of functioning in relation to vocational preparation. In this research, we have designed a framework to evaluate and train the visual working memory and attention level of users by using a humanoid robot and a brain headband sensor. The humanoid robot generates a sequence of colors and the user performs the task by arranging the colored blocks in the same order. In addition, a task-switching paradigm is used to switch between the tasks and colors to give a new instruction to the user by the robot. The humanoid robot displays guidance error detection information, observes the performance of users during the assessment and gives instructive feedback to them. This research describes the profile of cognitive and behavioral characteristics associated with visual working memory skills, selective attention and ways of supporting the learning needs of workers affected by this problem. Finally, the research concludes the relationships between visual working memory and attentional level during different level of the assessment.

Using Humanoid Robot to Instruct and Evaluate Performance of a Physical Task.

In this paper, we present a tool to assess users ability to change tasks. To do this, we use a variation of the Box and Blocks Test. In this version, a humanoid robot instructs a user to perform a task involving the movement of certain colored blocks. The robot changes randomly change the color of blocks that the user is supposed to move. Canny Edge Detection and Hough Transformation are used to assess user perform the robots built-in camera. This will allow the robot to inform the user and keep a log of their progress. We present this method for monitoring user progress by describing how the moved blocks are detected. We also present the results of a pilot study where users used this system to perform the task. Preliminary results show that users do not perform differently when the task is changed in this scenario.

Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection

Human face detection plays an essential role in the first stage of face processing applications. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate.