Facial expression recognition on partially occluded faces using component based ensemble stacked CNN

Bellamkonda, Sivaiah; Gopalan, N. P.; Mala, C.; Settipalli, Lavanya

doi:10.1007/s11571-022-09879-y

Cited by 7 publications

(1 citation statement)

References 69 publications

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“…According to many studies in the literature [21][22][23][24][25][26][27][28][29][30][31][32], disadvantages of single-module biometric systems include poor recognition mainly due to intra-class variability and intra-class similarity and disadvantages of face recognition and detection. It increases the complexity of biometrics.…”

Section: Introductionmentioning

confidence: 99%

Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Avishek Dey, Dr. Satish Chander

2023

tjjpt

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Smile detection plays key role in recognizing human facial expressions, especially in inter-personal relations. In modern biometrics and security applications, face recognition is widely used. In real-life applications, automated face recognition has always been challenging because people express themselves differently. Real-world scenarios present many challenges, such as low-quality images, temporal variations, and disguises that alter facial characteristics. Face recognition has been accomplished using deep learning frameworks, but there are still several challenges to overcome, such as expression variations and lack of training data. In these methods, margins are used to enforce intra-class compactness and inter-class discrepancy to prevent overfitting. Multimodal facial expression detection is used for real-time face recognition to solve the aforementioned problems. First, we utilizes the Prewitt edge detector for eyes, nose, mouth, and brow detection, and design a deep transfer learning model to extract deep features from face images. Next, feature optimization is done by harpy eagle search optimization (HESO) to selects optimal best among multiple features which reduce data dimensionality problem. Then, we employ a semi-region based convolutional neural network (SR-CNN) to analyzes the facial expressions from multimodalities such as eyes (blinking), nose (shapes), mouth (smiling), and brow (shapes). The analyzed facial expressions and corresponding input images are fed into the benchmark SVM classifier for face recognition. The facial expression based face recognition framework is most suited for real-time scenario like video surveillance. Finally, we validate the performance our framework using benchmark multi-modal Cohn-Kanade and real-time 5671 person images datasets. The simulation results show that the proposed framework can effectively suppress data dimensionality issues and achieve high recognition rate compare to state-of-art frameworks.

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Section: Introductionmentioning

confidence: 99%

Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Avishek Dey, Dr. Satish Chander

2023

tjjpt

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Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions

Selvi,

Parvathy

2024

Intl J of Devlp Neuroscience

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The term ‘autism spectrum disorder’ describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns and poor social interaction. Understanding mental states from FEs is crucial for interpersonal interaction and social interaction. But when there are occlusions like glasses, facial hair or self‐occlusion, it becomes harder to identify facial expressions accurately. This research tackles the issue of identifying facial expressions when parts of the face are occluded and suggests an innovative technique to tackle this difficulty. Creating a strong framework for facial expression recognition (FER) that better handles occlusions and increases recognition accuracy is the goal of this research. Therefore, we propose novel Improved DenseNet‐based Residual Cross‐Attention Transformer (IDenseNet‐RCAformer) system to tackle the partial occlusion FER problem in autism patients. The recognition framework's efficacy is assessed using four datasets of facial expressions, and some preprocessing procedures are conducted to increase the expression recognition efficiency. After that, when recognizing expressions, a simple argmax function is applied to get a forecasted landmark position from a heatmap. Then feature extraction phase, local and global representation are captured from preprocessed images by adopting Inception‐ResNet‐V2 approach, Cross‐Attention Transformer, respectively. Moreover, both features are fused by employing the FusionNet method, thereby enhancing system's training speed and precision. After the features are extracted, an improved DenseNet mechanism is applied to efficiently recognize some variety of facial expressions in partially occluded autism patients. A number of performance metrics are determined and analysed to demonstrate the proposed approach's effectiveness, where the IDenseNet‐RCAformer performs best with an accuracy of 98.95%. According to the experimental findings, the proposed framework significantly outperforms the prior recognition frameworks in terms of recognition outcomes.

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EFL-LCNN: Enhanced face localization augmented light convolutional neural network for human emotion recognition

Bellamkonda

Settipalli

2023

Multimed Tools Appl

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Facial expression recognition on partially occluded faces using component based ensemble stacked CNN

Cited by 7 publications

References 69 publications

Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions

EFL-LCNN: Enhanced face localization augmented light convolutional neural network for human emotion recognition

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