Rahadian Yusuf

Evolving an emotion recognition module for an intelligent agent using genetic programming and a genetic algorithm

AbstractMost studies use the facial expression to recognize a user’s emotion; however, gestures, such as nodding, shaking the head, or stillness can also be indicators of the user’s emotion. In our research, we use the facial expression and gestures to detect and recognize a user’s emotion. The pervasive Microsoft Kinect sensor captures video data, from which several features representing facial expressions and gestures are extracted. An in-house extensible markup language-based genetic programming engine (XGP) evolves the emotion recognition module of our system. To improve the computational performance of the recognition module, we implemented and compared several approaches, including directed evolution, collaborative filtering via canonical voting, and a genetic algorithm, for an automated voting system. The experimental results indicate that XGP is feasible for evolving emotion classifiers. In addition, the obtained results verify that collaborative filtering improves the generality of recognition. From a psychological viewpoint, the results prove that different people might express their emotions differently, as the emotion classifiers that are evolved for particular users might not be applied successfully to other user(s).

Evolving Emotion Recognition Module for Intelligent Agent

An emotion recognition module is crucial in designing a computer agent that is capable of interacting with emotional expressions. Under-standing user’s current emotion can be achieved by several methods, but current researches are either using still images, or sensors that are not pervasive. Usual approach is using a generalized classifier to recognize pattern of emotion features captured by sensors. Unlike most researches, this research focuses on pervasive sensors and a single user, using evolution algorithm. This research also discusses about the classifier evolutions using Genetic Programming, and comparing several directed evolutions in evolving the emotion recognition module.

Human gait analysis based on biological motion and evolutionary computing

Abstract Human motion has already deeply affected many aspects of psychological and social research. On the other hand, because of the huge challenges and new dimensions of its increasingly extreme applications, this field remains an inspiring area in which to explore rich possibilities in the fields of artificial intelligence and bio-informatics. In this research, we investigated a novel approach to identify individuals based on their gaits. Furthermore, we investigated a new avenue of the research toward the biometric identification of humans that involves the classification of human gait using the power of genetic programming (GP). Moreover, we also propose an approach that applies collaborative filter using multiple evolved classifiers to address the challenges of non-determinism and insufficient generality of GP.

Application of genetic programming and genetic algorithm in evolving emotion recognition module

This paper will discuss about implementation of a voting system and weighted credibility to augment evolution process of an emotion recognition module. The evolution process of the emotion recognition module is one part of ongoing research on designing an intelligent agent capable of emotion recognition, interaction, and expression. Genetic programming evolves the classifiers, while genetic algorithm evolves the weighted credibility as a modification of parallel voting systems. The experimental results suggest that the implementation of weighted credibility evolution improves the performance of training, in the form of significantly reduced training time needed.

Human Recognition based on Gait Features and Genetic Programming

Human walking has always been the curious field of research for different disciple of social and information science. The study of human walk or human gait in association with different behaviors and emotions has not only fascinated social science researchers, but its uniqueness has also attracted many computer scientists to work in this arena for the quest of uncovering reliable mechanisms of biometric identification. In this research, we used a novel method for human identification based on inferring the relationship between the human gait features via genetic programming. Moreover, we focus on generating the unique numerical signature that is similar for different locomotion gaits of a particular individual but different across different individuals.

Steering oscillation as an effect of cognitive delay in human drivers

We proposed an approach of applying genetic programming (GP) to automatically develop a driving agent - as a model of a human driver - that optimally steers a realistically simulated car with an instant, non-latent steering response. We verified the hypothesis that introducing a delay in steering response of the evolved model of human driver results in well-expressed steering oscillations. The detection of these oscillations could pave the way for an early-warning of the inadequate cognitive load (as an underlying cause of such delays) of driver in normal driving conditions - well before an urgent response to an eventual hazardous traffic situation is required.

Stand-alone application of user-specific emotion recognition model to improve real-time online voice communication using expressive avatar

One of the objectives of this research is to explore and investigate on how to improve online voice communication. We use our previously developed user-specific emotion recognition model to recognize users emotion during communication and then to express it using an avatar to show to the partner. Another objective is to investigate the performance of our model in real-time environment using a stand-alone application developed by a party who is not proficient with emotion recognition. The results were: the said party can develop a stand-alone application despite not having much knowledge on emotion recognition using our model, and that real-time recognition can be achieved. The questionnaire results from the subjects also suggest that using expressive avatar improves communication, and that user-specific approach gives better performance compared to general approach.

Individuality and user-specific approach in adaptive emotion recognition model

This study aims at developing an intelligent agent that can recognize user-specific emotions and can self-evolve. Previous studies have explored several methods to develop the model and improve the results while maintaining the feasibility of real-time implementation for later stages. We evolved the emotion recognition module by using Genetic Programming (GP) and explored several optimizations. We investigated and compared the evolution of a unique classifier (evolved from data from a single specific subject only), the evolution of a general classifier (evolved from data from multiple subjects), and the evolution of an adaptive classifier by implementing incremental GP (evolved incrementally, first from multiple subjects and then from a single specific subject). We conducted the experiments by using the same budget in terms of evolution sessions to obtain the best programs for a fair comparison between general approach, user-specific approach, and adaptive approach. We then performed repeated experiments to verify the robustness of the method. From the results, we concluded that, on an average, adaptive approach not only resulted in faster evolution time, but also achieved better accuracy in emotion recognition.