Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding

Texture based feature extraction using symbol patterns for facial expression recognition

Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP1, SP2 and SP3) have been proposed for facial feature extraction. SP1 generates one pattern value by comparing eight pixels within a 3× 3 neighborhood, whereas, SP2 and SP3 generates two pattern values each by comparing twelve and sixteen pixels within a 5× 5 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

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.

End-to-End Training for Compound Expression Recognition

For a long time, expressions have been something that human beings are proud of. That is an essential difference between us and machines. With the development of computers, we are more eager to develop communication between humans and machines, especially communication with emotions. The emotional growth of computers is similar to the growth process of each of us, starting with a natural, intimate, and vivid interaction by observing and discerning emotions. Since the basic emotions, angry, disgusted, fearful, happy, neutral, sad and surprised are put forward, there are many researches based on basic emotions at present, but few on compound emotions. However, in real life, people’s emotions are complex. Single expressions cannot fully and accurately show people’s inner emotional changes, thus, exploration of compound expression recognition is very essential to daily life. In this paper, we recommend a scheme of combining spatial and frequency domain transform to implement end-to-end joint training based on model ensembling between models for appearance and geometric representations learning for the recognition of compound expressions in the wild. We are mainly devoted to digging the appearance and geometric information based on deep learning models. For appearance feature acquisition, we adopt the idea of transfer learning, introducing the ResNet50 model pretrained on VGGFace2 for face recognition to implement the fine-tuning process. Here, we try and compare two minds, one is that we utilize two static expression databases FER2013 and RAF Basic for basic emotion recognition to fine tune, the other is that we fine tune the model on the input three channels composed of images generated by DWT2 and WAVEDEC2 wavelet transforms based on rbio3.1 and sym1 wavelet bases respectively. For geometric feature acquisition, we firstly introduce a densesift operator to extract facial key points and their histogram descriptions. After that, we introduce deep SAE with a softmax function, stacked LSTM and Sequence-to-Sequence with stacked LSTM and define their structures by ourselves. Then, we feed the salient key points and their descriptions into three models to train respectively and compare their performances. When the model training for appearance and geometric features learning is completed, we combine the two models with category labels to achieve further end-to-end joint training, considering that ensembling models, which describe different information, can further improve recognition results. Finally, we validate the performance of our proposed framework on an RAF Compound database and achieve a recognition rate of 66.97%. Experiments show that integrating different models, which express different information, and achieving end-to-end training can quickly and effectively improve the performance of the recognition.

Visual and Thermal Image Processing for Facial Specific Landmark Detection to Infer Emotions in a Child-Robot Interaction

Child-Robot Interaction (CRI) has become increasingly addressed in research and applications. This work proposes a system for emotion recognition in children, recording facial images by both visual (RGB-red, green and blue) and Infrared Thermal Imaging (IRTI) cameras. For this purpose, the Viola-Jones algorithm is used on color images to detect facial regions of interest (ROIs), which are transferred to the thermal camera plane by multiplying a homography matrix obtained through the calibration process of the camera system. As a novelty, we propose to compute the error probability for each ROI located over thermal images, using a reference frame manually marked by a trained expert, in order to choose that ROI better placed according to the expert criteria. Then, this selected ROI is used to relocate the other ROIs, increasing the concordance with respect to the reference manual annotations. Afterwards, other methods for feature extraction, dimensionality reduction through Principal Component Analysis (PCA) and pattern classification by Linear Discriminant Analysis (LDA) are applied to infer emotions. The results show that our approach for ROI locations may track facial landmarks with significant low errors with respect to the traditional Viola-Jones algorithm. These ROIs have shown to be relevant for recognition of five emotions, specifically disgust, fear, happiness, sadness, and surprise, with our recognition system based on PCA and LDA achieving mean accuracy (ACC) and Kappa values of 85.75% and 81.84%, respectively. As a second stage, the proposed recognition system was trained with a dataset of thermal images, collected on 28 typically developing children, in order to infer one of five basic emotions (disgust, fear, happiness, sadness, and surprise) during a child-robot interaction. The results show that our system can be integrated to a social robot to infer child emotions during a child-robot interaction.