Ensemble of Machine Learning Models for an Improved Facial Emotion Recognition

Pulido-Castro, Sergio; Palacios-Quecan, Nubia; Ballen-Cardenas, Michelle P.; Cancino-Suarez, Sandra; Rizo-Arévalo, Alejandra; Lopez, Jennifer

doi:10.1109/urucon53396.2021.9647375

Cited by 5 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 23 ], the Kazemi and Sullivan approach [ 14 ] is employed to detect facial physiological information from face shapes in videos frames, depending on the features of eyes, nose, mouth, and face contour. The approach of Sergio Pulido-Castro et al [ 24 ] is also based on [ 14 ] and its aim is to align the landmarks on a face and recognize facial expressions and emotions. The acceleration and robustness techniques presented in this paper for the original 2D shape alignment method with its limitations (no support for 3D models, occlusion and tilt higher than ±20°) can be exploited with no modifications in the approaches referenced above.…”

Section: Introductionmentioning

confidence: 99%

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

Christakos

Petrellis

Mousouliotis

et al. 2023

Sensors

View full text Add to dashboard Cite

A high-performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. The popular Ensemble of Regression Trees (ERTs) machine learning method has been employed for the alignment of 68 facial landmarks. Open-source implementation of ERTs for facial shape alignment has been ported to different platforms and adapted for the acceleration of the frame processing speed using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without sacrificing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Hence, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other machine learning applications that require large data transfers to their kernels with low reusability.

show abstract

Section: Introductionmentioning

confidence: 99%

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

Christakos

Petrellis

Mousouliotis

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In [19], the Kazemi and Sullivan approach [11] is employed to detect faces during video frame processing and depending on the features of eyes, nose, mouth, and face contour, the appropriate facial physiological information is extracted. The approach of Sergio Pulido-Castro et al [20] is also based on [11] and it targets to align the landmarks on a face and recognize facial expressions and emotions.…”

Section: Introductionmentioning

confidence: 99%

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

Christakos¹,

Petrellis²,

Mousouliotis³

et al. 2023

Preprint

View full text Add to dashboard Cite

A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.

show abstract

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

Banos,

Comas-González,

Medina

et al. 2024

International Journal of Medical Informatics

View full text Add to dashboard Cite

Ensemble of Machine Learning Models for an Improved Facial Emotion Recognition

Cited by 5 publications

References 8 publications

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

Contact Info

Product

Resources

About