ASSP4MI2016:2nd international workshop on advancements in social signal processing for multimodal interaction (workshop summary)

This paper gives a summary of the 2nd International Workshop on Advancements in Social Signal Processing for Multimodal Interaction (ASSP4MI). Following our successful 1st International Workshop on Advancements in Social Signal Processing for Multimodal Interaction, held during ICMI- 2015, we proposed the 2nd ASSP4MI workshop during ICMI- 2016. The topics addressed and discussions fostered during last year's workshop are considered very relevant and alive in the research community. In this year's workshop, we continued addressing important topics and fostering fruitful discussions among researchers from different disciplines working in the fields of Social Signal Processing (SSP) and multimodal interaction.</p

Development of a robust multi-scale featured local binary pattern for improved facial expression recognition

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1)and LBP(8,2)and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade(CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces(KDEF)dataset

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors