A Deep Autoencoder With Novel Adaptive Resolution Reconstruction Loss for Disentanglement of Concepts in Face Images

Abdolahnejad, Mahla; Liu, Peter

doi:10.1109/tim.2022.3165261

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Autoencoders (AE) have gained popularity as an unsupervised neural network that can reconstruct data while minimizing the error between input and output [12] with an encoder mapping the input to a hidden representation through a nonlinear function and a decoder reconstructing the input [13]. AE has been shown to perform well in facial reconstruction, resolving issues of partial occlusion [14]. In this paper, we present a novel and advanced model, named Sparse Autoencoders for Facial Expressions-based Pain Assessment (SAFEPA), which builds upon our previous FEAPAS described in [9].…”

Section: Introductionmentioning

confidence: 99%

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Alghamdi

Alaghband

2023

Applied Sciences

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Accurately assessing the intensity of pain from facial expressions captured in videos is crucial for effective pain management and critical for a wide range of healthcare applications. However, in uncontrolled environments, detecting facial expressions from full left and right profiles remains a significant challenge, and even the most advanced models for recognizing pain levels based on facial expressions can suffer from declining performance. In this study, we present a novel model designed to overcome the challenges posed by full left and right profiles—Sparse Autoencoders for Facial Expressions-based Pain Assessment (SAFEPA). Our model utilizes Sparse Autoencoders (SAE) to reconstruct the upper part of the face from the input image, and feeds both the original image and the reconstructed upper face into two pre-trained concurrent and coupled Convolutional Neural Networks (CNNs). This approach gives more weight to the upper part of the face, resulting in superior recognition performance. Moreover, SAFEPA’s design leverages CNNs’ strengths while also accommodating variations in head poses, thus eliminating the need for face detection and upper-face extraction preprocessing steps needed in other models. SAFEPA achieves high accuracy in recognizing four levels of pain on the widely used UNBC-McMaster shoulder pain expression archive dataset. SAFEPA is extended for facial expression recognition, where we show it to outperform state-of-the-art models in recognizing seven facial expressions viewed from five different angles, including the challenging full left and right profiles, on the Karolinska Directed Emotional Faces (KDEF) dataset. Furthermore, the SAFEPA system is capable of processing BioVid Heat Pain datasets with an average processing time of 17.82 s per video (5 s in length), while maintaining a competitive accuracy compared to other state-of-the-art pain detection systems. This experiment demonstrates its applicability in real-life scenarios for monitoring systems. With SAFEPA, we have opened new possibilities for accurate pain assessment, even in challenging situations with varying head poses.

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