Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition

Mohamed Tahir; Md Azher Uddin; Joolekha Bibi Joolee

doi:10.1007/978-981-96-1758-6_17

Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition

Mohamed Tahir, Md Azher Uddin^*, Joolekha Bibi Joolee

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Facial expressions are the primary means of communicating human emotions, and their interpretation has earned significant interest from researchers due to their wide range of practical applications. However, there is a lack of research on the simultaneous recognition of spontaneous macro-expressions and micro-expressions. This paper aims to develop an end-to-end framework for the effective recognition of both spontaneous macro- and micro-expressions. The proposed framework utilizes Volume Local Directional Number (VLDN) for spatiotemporal feature extraction and ResNet101 for extracting deep spatial features from each frame. Additionally, we designed a Gated Recurrent Unit (GRU) to effectively learn the spatiotemporal features. Finally, we conduct comprehensive experiments on the CAS(ME)² dataset to demonstrate the performance of our proposed method.

Original language	English
Title of host publication	Proceedings of International Conference on Information Technology and Applications. ICITA 2024
Editors	Abrar Ullah, Sajid Anwar
Publisher	Springer
Pages	193-203
Number of pages	11
ISBN (Electronic)	9789819617586
ISBN (Print)	9789819617579
DOIs	https://doi.org/10.1007/978-981-96-1758-6_17
Publication status	Published - 15 Jun 2025
Event	18th International Conference on Information Technology and Applications 2024 - Sydney, Australia Duration: 17 Oct 2024 → 19 Oct 2024 https://2024.icita.world/#/

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1248
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	18th International Conference on Information Technology and Applications 2024
Abbreviated title	ICITA 2024
Country/Territory	Australia
City	Sydney
Period	17/10/24 → 19/10/24
Internet address	https://2024.icita.world/#/

Keywords

Facial expression
Gated Recurrent Unit
ResNet101
Volume Local Directional Number

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-1758-6_17

Cite this

Tahir, M., Uddin, M. A., & Joolee, J. B. (2025). Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition. In A. Ullah, & S. Anwar (Eds.), Proceedings of International Conference on Information Technology and Applications. ICITA 2024 (pp. 193-203). (Lecture Notes in Networks and Systems; Vol. 1248). Springer. https://doi.org/10.1007/978-981-96-1758-6_17

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title = "Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition",

abstract = "Facial expressions are the primary means of communicating human emotions, and their interpretation has earned significant interest from researchers due to their wide range of practical applications. However, there is a lack of research on the simultaneous recognition of spontaneous macro-expressions and micro-expressions. This paper aims to develop an end-to-end framework for the effective recognition of both spontaneous macro- and micro-expressions. The proposed framework utilizes Volume Local Directional Number (VLDN) for spatiotemporal feature extraction and ResNet101 for extracting deep spatial features from each frame. Additionally, we designed a Gated Recurrent Unit (GRU) to effectively learn the spatiotemporal features. Finally, we conduct comprehensive experiments on the CAS(ME)2 dataset to demonstrate the performance of our proposed method.",

keywords = "Facial expression, Gated Recurrent Unit, ResNet101, Volume Local Directional Number",

author = "Mohamed Tahir and Uddin, \{Md Azher\} and Joolee, \{Joolekha Bibi\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 18th International Conference on Information Technology and Applications 2024, ICITA 2024 ; Conference date: 17-10-2024 Through 19-10-2024",

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language = "English",

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series = "Lecture Notes in Networks and Systems",

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Tahir, M, Uddin, MA & Joolee, JB 2025, Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition. in A Ullah & S Anwar (eds), Proceedings of International Conference on Information Technology and Applications. ICITA 2024. Lecture Notes in Networks and Systems, vol. 1248, Springer, pp. 193-203, 18th International Conference on Information Technology and Applications 2024, Sydney, New South Wales, Australia, 17/10/24. https://doi.org/10.1007/978-981-96-1758-6_17

Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition. / Tahir, Mohamed; Uddin, Md Azher; Joolee, Joolekha Bibi.
Proceedings of International Conference on Information Technology and Applications. ICITA 2024. ed. / Abrar Ullah; Sajid Anwar. Springer, 2025. p. 193-203 (Lecture Notes in Networks and Systems; Vol. 1248).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition

AU - Tahir, Mohamed

AU - Uddin, Md Azher

AU - Joolee, Joolekha Bibi

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025/6/15

Y1 - 2025/6/15

N2 - Facial expressions are the primary means of communicating human emotions, and their interpretation has earned significant interest from researchers due to their wide range of practical applications. However, there is a lack of research on the simultaneous recognition of spontaneous macro-expressions and micro-expressions. This paper aims to develop an end-to-end framework for the effective recognition of both spontaneous macro- and micro-expressions. The proposed framework utilizes Volume Local Directional Number (VLDN) for spatiotemporal feature extraction and ResNet101 for extracting deep spatial features from each frame. Additionally, we designed a Gated Recurrent Unit (GRU) to effectively learn the spatiotemporal features. Finally, we conduct comprehensive experiments on the CAS(ME)2 dataset to demonstrate the performance of our proposed method.

AB - Facial expressions are the primary means of communicating human emotions, and their interpretation has earned significant interest from researchers due to their wide range of practical applications. However, there is a lack of research on the simultaneous recognition of spontaneous macro-expressions and micro-expressions. This paper aims to develop an end-to-end framework for the effective recognition of both spontaneous macro- and micro-expressions. The proposed framework utilizes Volume Local Directional Number (VLDN) for spatiotemporal feature extraction and ResNet101 for extracting deep spatial features from each frame. Additionally, we designed a Gated Recurrent Unit (GRU) to effectively learn the spatiotemporal features. Finally, we conduct comprehensive experiments on the CAS(ME)2 dataset to demonstrate the performance of our proposed method.

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BT - Proceedings of International Conference on Information Technology and Applications. ICITA 2024

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ER -

Deep Spatiotemporal Network-Based Spontaneous Macro- and Micro-facial Expression Recognition

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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