arXiv Machine Learning July 20, 2026 By Chethiya Galkaduwa

An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition

Read the original on arXiv Machine Learning →

arXiv:2607. 15288v1 Announce Type: cross Abstract: Facial expression recognition is an important computer vision task with applications in human--computer interaction, mental health monitoring, driver alert systems, and behavioral analysis.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

computer-vision

arXiv Computer Vision

Sep 15

Event-Level Emotion Recognition in the Wild Using Deep Facial Expression Analysis

arXiv:2609.13854v1 Announce Type: new Abstract: Facial emotion recognition (FER) in real-world environments remains challenging due to unconstrained imaging conditions, including multiple faces, occl...

By Aleksandr Semerikov, Pakizar Shamoi

fine-tuning

arXiv AI

Jul 15

HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition

arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.

By Aleksei Bakin, Andrey V. Savchenko

fine-tuning multimodal safety

arXiv Machine Learning

Aug 11

Sparse Attention to Emotion: Efficient Facial Emotion Recognition via Token Reduction

arXiv:2608. 08873v1 Announce Type: cross Abstract: Facial Emotion Recognition (FER) is an important task that has significant implications across various fields such as biometrics, health, and human-computer interaction.

By Aya Manel Zitouni, Aicha Zenakhri, Karim Haroun, Larbi Boubchir

llms computer-vision

arXiv AI

Jul 24

Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks

arXiv:2607. 20820v1 Announce Type: new Abstract: Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive.

By Christian Arzate Cruz, Stefanos Gkikas, Houshyar Asadi

efficiency safety

arXiv Computer Vision

Sep 11

Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition

The paper introduces SV-GCN, a single-stream multi-feature fusion framework for 3D skeleton-based gait emotion recognition that incorporates temporal invariance. It uses intra-frame relative motion features to remove frame-rate sensitivity and embeds heterogeneous cues at shallow layers for early fusion, avoiding multi-stream complexity. A global mask-guided valid-frame spatio-temporal graph convolution module further enhances robustness to variable-length sequences and differing frame rates, achieving state‑of‑the‑art performance on the E‑Gait dataset and strong generalization across sequence lengths.

By Shirong Lyu, Silu Quan, Yixuan Ding, Chengpeng Wang

benchmarks

arXiv Machine Learning

Jun 25

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.

By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal

fine-tuning benchmarks safety