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Matti Pietikäinen

Matti Pietikäinen

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Computer Science
Finland
2026

D-Index & Metrics

Computer Science

D-Index
95
Citations
86499
World Ranking
451
National Ranking
1

Research.com Recognitions

  • 2026 - Research.com Computer Science in Finland Leader Award
  • 2025 - Research.com Computer Science in Finland Leader Award
  • 2023 - Research.com Computer Science in Finland Leader Award
  • 2022 - Research.com Computer Science in Finland Leader Award
  • 2018 - IAPR King-Sun Fu Prize For fundamental contributions to texture analysis and facial image analysis.
  • 1994 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to machine vision and its applications in industry and service to the IAPR

Overview

Matti Pietikäinen is affiliated with the University of Oulu in Finland. Their research primarily revolves around computer science, with a focus on computer vision and pattern recognition. The scientist's work also spans artificial intelligence and radiology, nuclear medicine, and imaging.

Their research addresses multiple topics including advanced neural network applications, COVID-19 diagnosis using AI, domain adaptation and few-shot learning, advanced image and video retrieval techniques, image retrieval and classification techniques, video surveillance and tracking methods, as well as adversarial robustness in machine learning.

  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Image Retrieval and Classification Techniques
  • Video Surveillance and Tracking Methods
  • Adversarial Robustness in Machine Learning

Matti Pietikäinen has contributed to the following notable publications:

  • Dynamic Binary Neural Network by Learning Channel-Wise Thresholds, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond, 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Matti Pietikäinen include Zhuo Su, Li Liu, Jiehua Zhang, Yanghe Feng, and Xin Lü.

  • Zhuo Su
  • Li Liu
  • Jiehua Zhang
  • Yanghe Feng
  • Xin Lü

Matti Pietikäinen has published work in venues such as ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) and arXiv (Cornell University).

  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • arXiv (Cornell University)

The scientist has received recognition through awards including the IAPR King-Sun Fu Prize in 2018, acknowledging fundamental contributions to texture analysis and facial image analysis. They were also named a Fellow of the International Association for Pattern Recognition (IAPR) in 1994 for contributions to machine vision and its applications in industry as well as service to the IAPR.

Best Publications

  • Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

    T. Ojala;M. Pietikainen;T. Maenpaa

  • A comparative study of texture measures with classification based on featured distributions

    Timo Ojala;Matti Pietikäinen;David Harwood

  • Face Description with Local Binary Patterns: Application to Face Recognition

    T. Ahonen;A. Hadid;M. Pietikainen

  • Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

    Guoying Zhao;M. Pietikainen

  • Face Recognition with Local Binary Patterns

    Timo Ahonen;Abdenour Hadid;Matti Pietikäinen

  • Adaptive document image binarization

    Jaakko J. Sauvola;Matti Pietikäinen

  • Deep Learning for Generic Object Detection: A Survey

    Li Liu;Li Liu;Wanli Ouyang;Xiaogang Wang;Paul W. Fieguth

  • Performance evaluation of texture measures with classification based on Kullback discrimination of distributions

    T. Ojala;M. Pietikainen;D. Harwood

  • A texture-based method for modeling the background and detecting moving objects

    M. Heikkila;M. Pietikainen

  • Description of interest regions with local binary patterns

    Marko Heikkilä;Matti Pietikäinen;Cordelia Schmid

  • WLD: A Robust Local Image Descriptor

    Jie Chen;Shiguang Shan;Chu He;Guoying Zhao

  • Outex - new framework for empirical evaluation of texture analysis algorithms

    T. Ojala;T. Maenpaa;M. Pietikainen;J. Viertola

  • Face spoofing detection from single images using micro-texture analysis

    Jukka Maatta;Abdenour Hadid;Matti Pietikainen

  • Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns

    Timo Ojala;Matti Pietikäinen;Topi Mäenpää

  • Facial expression recognition from near-infrared videos

    Guoying Zhao;Xiaohua Huang;Matti Taini;Stan Z. Li

  • A Spontaneous Micro-expression Database: Inducement, collection and baseline

    Xiaobai Li;Tomas Pfister;Xiaohua Huang;Guoying Zhao

  • An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification

    H. Kauppinen;T. Seppanen;M. Pietikainen

  • Remote Heart Rate Measurement from Face Videos under Realistic Situations

    Xiaobai Li;Jie Chen;Guoying Zhao;Matti Pietikäinen

  • Rotation-invariant texture classification using feature distributions

    Matti Pietikäinen;Timo Ojala;Zelin Xu

  • Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods

    Xiaobai Li;Xiaopeng Hong;Antti Moilanen;Xiaohua Huang

  • Unsupervised texture segmentation using feature distributions

    Timo Ojala;Matti Pietikäinen

Frequent Co-Authors

Guoying Zhao
Guoying Zhao University of Oulu
Abdenour Hadid
Abdenour Hadid University of Oulu
Timo Ojala
Timo Ojala University of Oulu
Paul Fieguth
Paul Fieguth University of Waterloo
Stan Z. Li
Stan Z. Li Westlake University
Xilin Chen
Xilin Chen University of Chinese Academy of Sciences
Tomas Pfister
Tomas Pfister Google (United States)
Wenming Zheng
Wenming Zheng Southeast University
Janne Heikkilä
Janne Heikkilä University of Oulu
Zhen Lei
Zhen Lei Chinese Academy of Sciences

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