Fernando De la Torre

12.3k citations
157 papers · 7.8k indexed · 2 hit papers · h-index 46

Fernando De la Torre

153 papers receiving 7.5k citations

Hit Papers

Supervised Descent Method and Its Applications to Face Al...1.3k20112026201620214008001.2k

Peers

Fernando De la Torre
Comparison fields: 5 of 183
  • Computer Vision and Pattern Recognition 5.3k
  • Signal Processing 1.3k
  • Human-Computer Interaction 638
  • Experimental and Cognitive Psychology 1.4k
  • Immunology and Allergy 490
Replace Hu Han with:
Hu Han China
Linlin Shen China
D.H. Ballard United States
Kang Ryoung Park South Korea
Zhen Lei China
Josephine Sullivan Sweden
Topi Mäenpää Finland
Carlos M. Travieso Spain
Guodong Guo China
Ran He China
Fernando De la Torre relative to Hu Han China Hu Han's profile →
Citations per field
00.5×20×40×60×81.7×
Hu Han · 1×
Citations per year

Countries citing papers authored by Fernando De la Torre

Since Specialization
Citations

This map shows the geographic impact of Fernando De la Torre'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 Fernando De la Torre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando De la Torre more than expected).

Fields of papers citing papers by Fernando De la Torre

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando De la Torre. 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 Fernando De la Torre. The network helps show where Fernando De la Torre may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Fernando De la Torre, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fernando De la Torre Line = papers co-authored together Fernando De la Torre links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Neural Synthesis of Binaural Speech From Mono Audio
202014
2 20175
3 201727
4 20164
5 201532
6 201439
7
Matrix Completion for Multi-label Image Classification
2011118
8 2011129
9 201139
10 201155
11 201114
12
Local Minima Embedding
20102
13
Canonical Time Warping for Alignment of Human Behavior
2009137
14 200822
15 2008180
16 20062
17 200532
18 200413
19 200170
20 198815

About Fernando De la Torre

Fernando De la Torre is a scholar working on Computer Vision and Pattern Recognition, Immunology and Allergy and Signal Processing, having authored 157 papers that have together received 7.8k indexed citations. Recurring topics across this work include Face recognition and analysis (51 papers), Face and Expression Recognition (42 papers), Human Pose and Action Recognition (27 papers), Video Surveillance and Tracking Methods (25 papers), Allergic Rhinitis and Sensitization (21 papers), Emotion and Mood Recognition (15 papers), Image Retrieval and Classification Techniques (14 papers) and Advanced Vision and Imaging (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (5.3k citations), Signal Processing (1.3k citations) and Human-Computer Interaction (638 citations). Fernando De la Torre has collaborated with scholars based in United States, Spain and China. Frequent co-authors include Xuehan Xiong, Jeffrey F. Cohn, Minh Hoai Nguyen, Feng Zhou, Wen-Sheng Chu, Feng Zhou, Minh Hoai, Gloria Bueno, Jesús Salido and Óscar Déniz. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Allergy and Clinical Immunology and IEEE Transactions on Image Processing.

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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