Farnaz Abtahi

611 total citations
12 papers, 289 citations indexed

About

Farnaz Abtahi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Farnaz Abtahi has authored 12 papers receiving a total of 289 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 5 papers in Experimental and Cognitive Psychology. Recurrent topics in Farnaz Abtahi's work include Emotion and Mood Recognition (5 papers), Reinforcement Learning in Robotics (4 papers) and Optimization and Search Problems (3 papers). Farnaz Abtahi is often cited by papers focused on Emotion and Mood Recognition (5 papers), Reinforcement Learning in Robotics (4 papers) and Optimization and Search Problems (3 papers). Farnaz Abtahi collaborates with scholars based in United States, Iran and Canada. Farnaz Abtahi's co-authors include Zhigang Zhu, Lijun Yin, Ian Fasel, Tony Ro, Wei Li, Wei Li, Mohammad Mehdi Ebadzadeh, M. R. Meybodi, Mohammad Reza Meybodi and Behrooz Masoumi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Machine Vision and Applications and PRZEGLĄD ELEKTROTECHNICZNY.

In The Last Decade

Farnaz Abtahi

12 papers receiving 278 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Farnaz Abtahi United States	8	199	174	47	44	35	12	289
Suja Palaniswamy India	13	163 0.8×	157 0.9×	92 2.0×	48 1.1×	39 1.1×	63	432
Hangyu Li China	7	253 1.3×	203 1.2×	52 1.1×	32 0.7×	24 0.7×	16	336
Qiangchang Wang China	5	273 1.4×	192 1.1×	49 1.0×	25 0.6×	32 0.9×	12	382
Arman Savran Türkiye	10	306 1.5×	230 1.3×	47 1.0×	52 1.2×	88 2.5×	22	418
Yante Li Finland	10	249 1.3×	263 1.5×	127 2.7×	58 1.3×	65 1.9×	20	475
Jinhyeok Jang South Korea	6	186 0.9×	156 0.9×	57 1.2×	31 0.7×	25 0.7×	14	273
Senya Polikovsky Japan	5	251 1.3×	193 1.1×	56 1.2×	39 0.9×	39 1.1×	9	352
Shuhang Wu China	4	190 1.0×	168 1.0×	63 1.3×	39 0.9×	42 1.2×	12	293
Dewi Yanti Liliana Indonesia	10	206 1.0×	192 1.1×	90 1.9×	42 1.0×	33 0.9×	47	374
Yuchi Liu China	7	141 0.7×	80 0.5×	58 1.2×	46 1.0×	32 0.9×	20	267

Countries citing papers authored by Farnaz Abtahi

Since Specialization

Citations

This map shows the geographic impact of Farnaz Abtahi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Farnaz Abtahi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farnaz Abtahi more than expected).

Fields of papers citing papers by Farnaz Abtahi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Farnaz Abtahi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Farnaz Abtahi. The network helps show where Farnaz Abtahi may publish in the future.

Co-authorship network of co-authors of Farnaz Abtahi

This figure shows the co-authorship network connecting the top 25 collaborators of Farnaz Abtahi. A scholar is included among the top collaborators of Farnaz Abtahi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Farnaz Abtahi. Farnaz Abtahi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Abtahi, Farnaz, et al.. (2018). EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(11). 2583–2596. 99 indexed citations

2.

Abtahi, Farnaz, et al.. (2018). Emotion Analysis Using Audio/Video, EMG and EEG: A Dataset and Comparison Study. 10–19. 19 indexed citations

3.

Abtahi, Farnaz, et al.. (2018). A recursive framework for expression recognition: from web images to deep models to game dataset. Machine Vision and Applications. 29(3). 489–502. 8 indexed citations

4.

Abtahi, Farnaz, et al.. (2017). EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection. 103–110. 87 indexed citations

5.

Li, Wei, et al.. (2016). An interactive facial-expression training platform for individuals with autism spectrum disorder. 1–3. 13 indexed citations

6.

Li, Wei, Farnaz Abtahi, & Zhigang Zhu. (2015). A Deep Feature based Multi-kernel Learning Approach for Video Emotion Recognition. 17 indexed citations

7.

Abtahi, Farnaz, et al.. (2015). A deep reinforcement learning approach to character segmentation of license plate images. 539–542. 16 indexed citations

8.

Masoumi, Behrooz, Mohammad Reza Meybodi, & Farnaz Abtahi. (2012). Learning Automata based Algorithms for Finding Optimal Policies in Fully Cooperative Markov Games. PRZEGLĄD ELEKTROTECHNICZNY. 280–289. 1 indexed citations

9.

Abtahi, Farnaz & Ian Fasel. (2011). Deep belief nets as function approximators for reinforcement learning. National Conference on Artificial Intelligence. 2–7. 16 indexed citations

10.

Abtahi, Farnaz, et al.. (2011). Information theoretic reward shaping for curiosity driven learning in POMDPs. 16. 1–7. 3 indexed citations

11.

Abtahi, Farnaz, et al.. (2008). Learning automata-based co-evolutionary genetic algorithms for function optimization. 1–5. 5 indexed citations

12.

Abtahi, Farnaz, et al.. (2008). Solving Multi-Agent Markov Decision Processes using learning automata. 3394. 1–6. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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