International Journal of Computer and Applications

This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA focuses on selecting the localized features from the expression frames using the partial $\boldsymbol {F}$ -test values, thereby reducing the within class variance and increasing the low between variance among different expression classes. For recognition, the hidden conditional random fields (HCRFs) model is utilized. HCRF is capable of approximating a complex distribution using a mixture of Gaussian density functions. To achieve optimum results, the system employs a hierarchical recognition strategy. Under these settings, expressions are divided into three categories based on parts of the face that contribute most toward an expression. During recognition, at the first level, SWLDA and HCRF are employed to recognize the expression category; whereas, at the second level, the label for the expression within the recognized category is determined using a separate set of SWLDA and HCRF, trained just for that category. In order to validate the system, four publicly available data sets were used, and a total of four experiments were performed. The weighted average recognition rate for the proposed FER approach was 96.37% across the four different data sets, which is a significant improvement in contrast to the existing FER methods.

Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields

Brain tumor segmentation based on a new threshold approach

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion

Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. In this study, we investigate how users' online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states. First, we extract a suite of multimodal time-series signals using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit emotions and emotion transitions. Next, we use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. Finally, we validate the effectiveness of our approach using two groups of recruited subjects.

https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9546/9334

Tackling mental health by integrating unobtrusive multimodal sensing

paper presents a new idea for detecting an unknown human face in input imagery and recognizing his/her facial expression. The objective of this research is to develop highly intelligent machines or robots that are mind implemented. A Facial Expression Recognition system needs to solve the following problems: detection and location of faces in a cluttered scene, facial feature extraction, and facial expression classification. The universally accepted five principal emotions to be realized are: Angry, Happy, Sad, Disgust and Surprise along with neutral. Principal Component Analysis (PCA) is implemented with Singular value decomposition (SVD) for Feature Extraction to determine principal emotions. The experiments show that the proposed facial expression recognition framework yields relatively little degradation in recognition rate due to facial images wearing glasses or loss of feature points during tracking.

/pdf/recognition-of-facial-expressions-with-principal-component-55on7t9yl4.pdf

Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition

The article focuses on the gentle introduction of Artificial Intelligence and the concepts of machine learning (ML) and deep learning (DL). The rapid developments made in DL techniques has motivated us to delve into this study. The concept of DL flourishing from basics theoretical concepts to applications. Deep neural networks are now state-of-the-art ML models commonly used in academia and industry in several fields, from image recognition to natural language processing. These advances have an immense potential for medical imaging technology, medical data processing, medical diagnostics and general healthcare. Our aim is two-fold: (1) the survey on DL approaches to medical images (2) the DL-based object detection approaches. The article delivers an effective description of the recent advances, advanced learning technologies and the platforms used for DL approaches. Object detection is the most explored and challenging concept in the field of computer vision systems. This field is receiving greater attention among the researchers since it covers real-time applications such as the face, pedestrian, text etc. The role of object detection is to detect the target objects presented in the image (or) video frames by appropriately classifying into their relevant classes. The review study of object detection begins with the recent works, the datasets used, and the real-time applications are explored from the learning strategies. Finally, the article investigates the challenges of the DL models and discusses promising future directions in both the research areas.

A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection

Image denoising is very important during enhancement of image. Original Image is generally corrupted with various types of noise. The noise present in the images may appear as additive or multiplicative components. The most challenging problem is removing that noise from an Image while preserving its details. Several noise removal techniques have been developed so far each having its own advantages and disadvantages. The focus of this paper is to study various spatial filters and to compare their performance in removing different types of noise. Here quantitative measure of comparison is provided by the Peak Signal to Noise Ratio (PSNR) parameter. General Terms Image Denoising, Spatial filtering.

/pdf/performance-comparison-of-various-image-denoising-filters-fphab67xo6.pdf

Performance Comparison of Various Image Denoising Filters under Spatial Domain

Recognition of a face under different emotions is a challenging subject. Human expression frequently changes with mood. during recognition of face, human may have any expression (neutral, happy, sad, angry, disgust or surprise) and thus, in case, where only neutral face expression has been stored in database, it become difficult to recognize the face of person which is under emotions other than neutral expression. The work done in this paper is twofold. First, local binary pattern (LBP) and center-symmetric local binary pattern (CS-LBP) has been applied to extract the local binary features of the image. Second, Euclidean distance, histogram intersection and chi-square distance is used for recognition of face. The performance is evaluated on the Japanese Female Facial Expression (JAFFE) database and results are compared in terms of recognition rate and time taken for processing. It has been observed that CS-LBP provides better recognition rate rather than LBP in case of different expressions of face.

Face recognition based on LBP and CS-LBP technique under different emotions

— Software professionals have found reuse as a powerful means to potentially overcome the situation of software crisis. Anything that is produced from a software development effort can potentially be reused and reduces the software development cost. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this research work, structural attributes of function oriented software components are explored using software metrics and reusability of the software is evaluated by experimenting five different Neural Network based approaches, taking the metric values as input. The calculated reusability value enables to identify a good quality code automatically. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) and are convincing enough to propose the system for the identification of reusable software components. So, the proposed model can be used to improve the productivity and quality of software development.

Nirvair Neeru

Papers

Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition

A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection

Performance Comparison of Various Image Denoising Filters under Spatial Domain

Face recognition based on LBP and CS-LBP technique under different emotions

Reusability Evaluation Model for Procedure Based Software Systems