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Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect(More)
Local representations became popular for facial affect recognition as they efficiently capture the image discontinuities, which play an important role for interpreting facial actions. We propose to use Local Zernike Moments (ZMs) [4] due to their useful and compact description of the image discontinuities and texture. Their main advantage in comparison to(More)
In this paper, we propose a new image representation called Local Zernike Moments (LZM) for face recognition. In recent years, local image representations such as Gabor and Local Binary Patterns (LBP) have attracted great interest due to their success in handling difficulties of face recognition. In this study, we aim to develop an alternative(More)
— Although automatic personality analysis has been studied extensively in recent years, it has not yet been adopted for real time applications and real life practices. To the best of our knowledge, this demonstration is a first attempt at predicting the widely used Big Five personality dimensions and a number of social dimensions from nonverbal behavioural(More)
In this demo session, a real-time automatic face detection and recognition system will be demonstrated. The system, which is implemented as a desktop application with a user interface, detects the faces in the images that are grabbed from a web camera using a cascaded classifier consisting of Modified Census Transform features. Then, using the same method,(More)
The Audio/Visual Mapping Personality Challenge and Workshop (MAPTRAITS) is a competition event that is organised to facilitate the development of signal processing and machine learning techniques for the automatic analysis of personality traits and social dimensions. MAPTRAITS includes two sub-challenges, the continuous space-time sub-challenge and the(More)
This study proposes a novel image representation and demonstrates its advantages when used for face recognition. The proposed representation is obtained by computing the global moments, which are popular tools for object and especially character recognition, locally at each pixel, thus by decomposing the image into a set of images corresponding to different(More)
The Audio/Visual Mapping Personality Challenge and Workshop (MAPTRAITS) is a competition event aimed at the comparison of signal processing and machine learning methods for automatic visual, vocal and/or audio-visual analysis of personality traits and social dimensions, namely, extroversion, agreeableness, conscientiousness, neuroticism, openness,(More)
Face images in a video sequence should be registered accurately before any analysis, otherwise registration errors may be interpreted as facial activity. Subpixel accuracy is crucial for the analysis of subtle actions. In this paper we present PSTR (Probabilistic Subpixel Temporal Registration), a framework that achieves high registration accuracy. Inspired(More)