Giuseppe Palestra

Improved Performance in Facial Expression Recognition Using 32 Geometric Features

Automatic facial expression recognition is one of the most interesting problem as it impacts on important applications in human-computer interaction area. Many applications in this field require real-time performance but not all the approach are suitable to satisfy this requirement. Geometrical features are usually the most light in terms of computational load but sometimes they exploits a huge number of features and do not cover all the possible geometrical aspect. In order to face up this problem, we propose an automatic pipeline for facial expression recognition that exploits a new set of 32 geometric facial features from a single face side covering a wide set of geometrical peculiarities. As a results, the proposed approach showed a facial expression recognition accuracy of 95,46% with a six-class expression set and an accuracy of 94,24% with a seven-class expression set.

Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment

Autism Spectrum Disorders (ASD) are a group of lifelong disabilities that affect peoples communication and understanding social cues. The state of the art witnesses how technology, and in particular robotics, may offer promising tools to strengthen the research and therapy of ASD. This work represents the first attempt to use machine-learning strategies during robot-ASD children interactions, in terms of facial expression imitation, making possible an objective evaluation of childrens behaviours and then giving the possibility to introduce a metric about the effectiveness of the therapy. In particular, the work focuses on the basic emotion recognition skills. In addition to the aforementioned applicative innovations this work contributes also to introduce a facial expression recognition (FER) engine that automatically detects and tracks the childs face and then recognize emotions on the basis of a machine learning pipeline based on HOG descriptor and Support Vector Machines. Two different experimental sessions were carried out: the first one tested the FER engine on publicly available datasets demonstrating that the proposed pipeline outperforms the existing strategies in terms of recognition accuracy. The second one involved ASD children and it was a preliminary exploration of how the introduction of the FER engine in the therapeutic protocol can be effectively used to monitor childrens behaviours.

A multimodal and multilevel system for robotics treatment of autism in children

Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorder (ASD). This paper presents a humanoid social robot-assisted behavioral system based on a therapeutic multilevel treatment protocol customized to improve eye contact, joint attention, symbolic play, and basic emotion recognition. In the system, the robot acts as a social mediator, trying to elicit specific behaviors in child, taking into account his/her multimodal signals. Statistical differences in eye contact and facial expression imitation behaviors after the use of the system are reported as preliminary results.

Social Robots in Postural Education: A New Approach to Address Body Consciousness in ASD Children

Autism Spectrum Disorders (ASD) represent one of the most prevalent developmental disorders among children with different level of impairments in social relationships, communication and imagination. In addition, impaired movement is also observed in individuals with ASD and recent studies consider this factor as a limitation for fully engagement in the social environment. In the present work, we propose a new approach to promote postural education in autistic children with the involvement of a humanoid social robot and the therapist in a triadic interaction environment to better understand their motor development and body consciousness.

Recognizing users feedback from non-verbal communicative acts in conversational recommender systems

Abstract Conversational recommender systems produce personalized recommendations of potentially useful items by utilizing natural language dialogues for detecting user preferences, as well as for providing recommendations. In this work we investigate the role of affective factors such as attitudes, emotions, likes and dislikes in conversational recommender systems and how they can be used as implicit feedback to improve the information filtering process. We thus developed a multimodal framework for recognizing the attitude of the user during their conversation with DIVA, a Dress-shopping InteractiVe Assistant aimed at recommending fashion apparel. Wee took into account speech prosody, body poses and facial expressions for providing implicit feedback to the system and for refining the recommendation accordingly. The shopping assistant has been embodied in the Social Robot NAO and has been tested in the dress shopping scenario. Our experimental results show that the proposed method is a promising way to implicitly profile the user and improve the performance of recommendations when explicit feedback is not available, thus demonstrating its effectiveness and viability.

Assistive Robot, RGB-D Sensor and Graphical User Interface to Encourage Communication Skills in ASD Population

The main feature of Autism Spectrum Disorders (ASDs) is the difficulty in communicating with others and struggling to maintain a functional contact with the environment. This work presents the implementation of a Graphical User Interface (GUI) for Digital PECS Therapy that will enable ASD population to overcome their impairments. The GUI was integrated with a depth sensor, to recognize hand gestures of autistic subjects, a monitor, where specific tools have been displayed, and a humanoid robot (Aldebaran Robotics NAO), used as a medium that will allow people with ASD to communicate their needs. Subjects can select the displayed pictures they want with hand movements, and the robot pronounces the represented objects. The system has been validated during therapeutic sessions with autistic subjects and the results are here reported and discussed supporting the idea that the presence of the robot helps to elicit triadic interactions in ASD.

Gaze-based Interaction with a Shop Window

This paper describes the first prototype of a gaze-based system designed for providing interactively information about dresses shown, on physical mannequins, in a shop window. Using the system the user may look at available sizes, colors, price and similar products. Due to the nature of such a system, the interaction must be touchless and natural. The developed solution uses Microsoft Kinect 2 as a device to achieve a natural gaze-based pointing approach. Results show that users evaluate positively the approach and felt positively engaged during the interaction.

Evaluating Natural Interaction with a Shop Window

This paper describes the evaluation of two interaction modalities for Active Fashion, the first prototype of system designed for providing interactively information about dresses shown on mannequins in a shop window. Using the system the user may look at available sizes, colors, price and similar products. Due to the nature of such a system, the interaction must be touch-less and natural. The developed solutions use Microsoft Kinect 2 as a device. The first modality is based on gestures while the second one is based on gaze pointing. Evaluation results show that even if the interaction did not result completely satisfying from the control point of view, users prefer the gaze-based approach and felt positively engaged during the interaction.

Improving Speech-Based Human Robot Interaction with Emotion Recognition

Several studies report successful results on how social assistive robots can be employed as interface in the assisted living domain. In this domain, a natural way to interact with robots is to use a speech. However, humans often use particular intonation in the voice that can change the meaning of the sentence. For this reason, a social assistive robot should have the capability to recognize the intended meaning of the utterance by reasoning on the combination of linguistic and acoustic analysis of the spoken sentence to really understand the user’s feedback. We developed a probabilistic model that is able to infer the intended meaning of the spoken sentence from the analysis of its linguistic content and from the output of a classifier able to recognise the valence and arousal of the speech prosody starting from dataset. The results showed that reasoning on the combination of the linguistic content with acoustic features of the spoken sentence was better than using only the linguistic component.

A Comparative Study on Soft Biometric Approaches to Be Used in Retail Stores

Soft biometric analysis aims at recognizing personal traits that provide some information about the individual. In this paper, we implemented and compared several approaches for soft biometric analysis in order to analyze humans soft biometric traits: age, gender, presence of eyeglasses and beard. Convolutional Neural Netoworks can be successfully used to understand soft biometric traits of passers-by looking at public displays and at shop windows.