Mohammed Moshiul Hoque

Effect of robot’s gaze behaviors for attracting and controlling human attention

Abstract Controlling someone’s attention can be defined as shifting his/her attention from the existing direction to another. To shift someone’s attention, gaining attention and meeting gaze are two most important prerequisites. If a robot would like to communicate a particular person, it should turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be effective in all situations, especially when the robot and the human are not facing each other or the human is intensely attending to his/her task. Therefore, the robot should perform some actions so that it can attract the target person and make him/her respond to the robot to meet gaze. In this paper, we present a robot that can attract a target person’s attention by moving its head, make eye contact through showing gaze awareness by blinking its eyes, and directs his/her attention by repeating its eyes and head turns from the person to the target object. Experiments using 20 human participants confirm the effectiveness of the robot actions to control human attention.

An empirical framework to control human attention by robot

Human attention control simply means that the shifting of ones attention from one direction to another. To shift someones attention, gaining attention and meeting gaze are two most important pre-requisites. If a person would like to communicate with another, the persons gaze should meet the receivers gaze, and they should make eye contact. However, it is difficult to set up eye contact when the two people are not facing each other in non-linguistic way. Therefore, the sender should perform some actions to capture the receivers attention so that they can meet face-to-face and establish eye contact. In this paper, we focus on what is the best action for a robot to attract human attention and how human and robot display gazing behavior each other for eye contact. In our system, the robot may direct its gaze toward a particular direction after making eye contact and the human will read the robots gaze. As a result, s/he will shift his/her attention to the direction indicated by the robot gaze. Experimental results show that the robots head motions can attract human attention, and the robots blinking when their gaze meet can make the human feel that s/he makes eye contact with the robot.

An Efficient Fuzzy Method for Bangla Handwritten Numerals Recognition

The object of the handwritten character recognition is the recognition of data that describe handwritten objects. On-line handwritten recognition deals with a time ordered sequence of data. This paper presents an on-line handwritten recognition system for recognizing the Bangla numerals using the fuzzy rule-base. Fuzzy logic has proved to be a powerful tool to represent imprecise and irregular patterns. The selection of the representative features, which describe the shapes and location of segments, is the core of the proposed approach. This paper describes a method that can be used to generate a fuzzy value database that describes the characters written by different individuals.

Controlling human attention through robot's gaze behaviors

Controlling someones attention can be defined as shifting his/her attention from the existing direction to another. However, it is not easy task for a robot to shift a particular humans attention if they are not in face-to-face situation. If the robot would like to communicate a particular person, it should turn its gaze to that person and make eye contact to establish mutual gaze. However, only such a turning action is not enough to set up eye contact when the robot and the target person are not facing each other. Therefore, the robot should perform some actions so that it can attract the target person and meet their gaze. In this paper, we present a robot that can attract a target persons attention by moving its head, make eye contact through showing gaze awareness by blinking its eyes, and establish joint attention by repeating its head turns from the person and the target object. Experiments using twenty human participants confirm the effectiveness of the robot actions to control human attention.

Model for controlling a target human's attention in multi-party settings

It is a major challenge in HRI to design a social robot that is able to direct a target humans attention towards an intended direction. For this purpose, the robot may first turn its gaze to him/her in order to establish eye contact. However, such a turning action of the robot may not in itself be sufficient to make eye contact with the target person in all situations, especially when the robot and the person are not facing each other or the human is intensely engaged in a task. In this paper, we propose a conceptual model of attention control with five phases: attention attraction, eye contact, attention avoidance, gaze back, and attention shift. We conducted two experiments to validate our model in human-robot interaction scenarios.

Attracting and controlling human attention through robot's behaviors suited to the situation

A major challenge is to design a robot that can attract and control human attention in various social situations. If a robot would like to communicate a person, it may turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be enough in all situations, especially when the robot and the human are not facing each other. In this paper, we present an attention control approach through robots behaviors that can attract a persons attention by three actions: head turning, head shaking, and uttering reference terms corresponding to three viewing situations in which the human vision senses the robot (near peripheral field of view, far peripheral field of view, and out of field of view). After gaining attention, the robot makes eye contact through showing gaze awareness by blinking its eyes, and directs the human attention by eye and head turning behaviors to share an object.

An integrated approach of attention control of target human by nonverbal behaviors of robots in different viewing situations

A major challenge in HRI is to design a social robot that can attract a target humans attention to control his/her attention toward a particular direction in various social situations. If a robot would like to initiate an interaction with a person, it may turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be enough to initiate an interaction in all situations, especially when the robot and the human are not facing each other or the human intensely attends to his/her task. In this paper, we propose a conceptual model of attention control with four phases: attention attraction, eye contact, attention avoidance, and attention shift. In order to initiate an attention control process, the robot first tries to gain the target participants attention toward it through head turning, or head shaking action depending on the three viewing situations where the robot is captured in his/her field of view (central field of view, near peripheral field of view, and far peripheral field of view). After gaining her/his attention, the robot makes eye contact only with the target person through showing gaze awareness by blinking its eyes, and directs her/his attention toward an object by turning its eyes and head cues. Moreover, the robot can show attention to aversion behaviors if non-target persons look at it. We design a robot based on the proposed approach, and it is confirmed as effective to control the target participants attention in experimental evaluation.

Bangla Numeral Recognition Engine (BNRE)

Numeral recognition is the process to classify the given character according to the predefined character class. This paper proposed a methodology for recognizing Bangla handwritten numerals which are based on fuzzy logic theory due to its low computational requirement. Every numeral is segmented and several features are extracted for each segment. In this paper, we use unique fuzzy rule base for each numeral. We have tested our engine for Bangla numerals considering various writing style and got more than 80% recognition accuracy.

Bangla vowel sign recognition by extracting the fuzzy features

This paper presents a fuzzy based Bangla vowel sign recognition system. Vowel and consonant are the main criteria to express any language. Exceptionally, Bangla has some vowel sign which are used to express Bangla word. Therefore, to recognize any Bangla word, at first it is necessary to recognize which vowel sign is used to express that word. Due to its low computational requirement and very high accuracy, fuzzy logic system is probably the most efficient method available for online vowel sign recognition. The most important task for this implementation is the building of the rule base using fuzzy logic that would describe the recognized vowel sign, which is more complicated as different people write the same vowel sign in completely different ways. In this paper, we propose a system that uses automatically generated fuzzy membership value that describes the handwritten Bangla vowel sign written by different individuals.

Attention control system considering the target person's attention level

In this paper, we propose an attention control system for social robots that attracts and controls the attention of a target person depending on his/her current attentional focus. The system recognizes the current task of the target person and estimates its level of focus by using the “task related behavior pattern” of the target human. The attention level is used to determine the suitable cues to attract the target persons attention toward the robot. The robot detects the interest or willingness of the target person to interact with it. Then, depending on the level of interest, the robot displays an awareness signal and shifts his/her attention to an intended goal direction.