Facial emotion recognition using landmarks

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Facial Emotion Recognition Using Landmarks

Facial emotion recognition (FER) is a critical area of research in computer vision and artificial intelligence, focusing on identifying human emotions through facial expressions. This article synthesizes recent advancements in FER using facial landmarks, highlighting various methodologies and their effectiveness.

Facial Landmarks in Emotion Recognition

Facial landmarks are specific points on the face, such as the corners of the eyes, the tip of the nose, and the edges of the mouth, which are used to analyze facial expressions. These landmarks are crucial for understanding the geometric structure of the face and its movements, which are indicative of different emotions.

Masked Face Emotion Recognition

The COVID-19 pandemic has necessitated the use of facial masks, complicating traditional FER methods that rely on the entire face. Recent research has focused on recognizing emotions from the upper part of the face, which remains visible when masks are worn. One study proposed a method using low-light image enhancement and feature analysis of the upper facial features with a convolutional neural network (CNN). This approach achieved an accuracy of 69.3% on the AffectNet dataset by focusing on the eyes, eyebrows, and forehead.

Geometric Features and Support Vector Machines

Another approach involves using geometric features derived from facial landmarks. A study introduced two new geometric features, landmark curvature and vectorized landmark, combined with a support vector machine (SVM) and a genetic algorithm for feature and parameter selection. This method demonstrated high accuracy on multiple datasets, outperforming traditional CNN-based methods in some cases.

Real-Time Landmark-Based Emotion Recognition

For real-time applications, a graph-based emotion recognition method using landmarks on the upper part of the face has been developed. This method involves several preprocessing steps, including face detection and landmark implementation, followed by model training on emotional classes. The proposed model achieved an overall accuracy of 91.2% for seven emotional classes in image-based applications.

Micro-Expression Recognition

Recognizing subtle facial micro-expressions, which are brief and involuntary, is more challenging than general facial expressions. A novel method using two-dimensional landmark feature maps (LFM) has been proposed to address this challenge. By transforming conventional coordinate-based landmark information into 2D image information, this method achieved superior performance on well-known micro-expression datasets.

Hybrid Features of Pixel and Geometry

Combining pixel-based features with geometric features of facial landmarks can enhance the discriminative power of emotional features. A hybrid feature extraction network, consisting of a Spatial Attention Convolutional Neural Network (SACNN) and Long Short-term Memory networks with Attention mechanism (ALSTMs), has been proposed. This method demonstrated high accuracy on several public databases, indicating its potential for improving human-robot interaction.

Real-Time Applications in Vehicles

For applications requiring low computational resources, such as in-vehicle systems, a fast FER algorithm using a hierarchical weighted random forest (WRF) classifier has been developed. This method detects facial landmarks and extracts geometric features, achieving performance comparable to deep learning methods but with significantly reduced processing costs.

Conclusion

Facial emotion recognition using landmarks is a rapidly evolving field with significant advancements in handling challenges such as masked faces, real-time processing, and subtle micro-expressions. By leveraging geometric features, hybrid models, and efficient algorithms, researchers are enhancing the accuracy and applicability of FER systems across various domains. These innovations hold promise for improving human-computer interaction, mental health monitoring, and safety applications.