What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Pattern recognition
His main research concerns Artificial intelligence, Pattern recognition, Local binary patterns, Computer vision and Image texture.
In general Pattern recognition, his work in Discriminative model and Linear discriminant analysis is often linked to Invariant and Fourier transform linking many areas of study.
Matti Pietikäinen interconnects Image processing, Texture, Binary pattern and Robustness in the investigation of issues within Local binary patterns.
Matti Pietikäinen focuses mostly in the field of Computer vision, narrowing it down to topics relating to Spoofing attack and, in certain cases, Linear classifier, Shape analysis, Image quality and Biometrics.
His Image texture research incorporates themes from Contextual image classification and Grayscale.
His Facial recognition system research includes elements of Facial expression and Feature vector.
His most cited work include:
- Multiresolution gray-scale and rotation invariant texture classification with local binary patterns (11350 citations)
- A comparative study of texture measures with classification based on featured distributions (5066 citations)
- Face Description with Local Binary Patterns: Application to Face Recognition (4503 citations)
What are the main themes of his work throughout his whole career to date?
Matti Pietikäinen mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Local binary patterns and Facial recognition system.
Histogram, Feature extraction, Image texture, Face and Image are among the areas of Artificial intelligence where Matti Pietikäinen concentrates his study.
His study brings together the fields of Contextual image classification and Image texture.
His Pattern recognition study combines topics in areas such as Pixel, Feature, Facial expression and Robustness.
His Local binary patterns study incorporates themes from Texture, Support vector machine, Feature vector and Scale-invariant feature transform.
His research in Facial recognition system intersects with topics in Spoofing attack and Biometrics.
He most often published in these fields:
- Artificial intelligence (83.58%)
- Pattern recognition (55.82%)
- Computer vision (49.85%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (83.58%)
- Pattern recognition (55.82%)
- Computer vision (49.85%)
In recent papers he was focusing on the following fields of study:
Matti Pietikäinen mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Discriminative model.
As part of his studies on Artificial intelligence, Matti Pietikäinen often connects relevant subjects like Machine learning.
His biological study spans a wide range of topics, including Feature, Pooling, Facial recognition system, Covariance matrix and Local binary patterns.
His Local binary patterns study combines topics from a wide range of disciplines, such as Vector quantization, Pixel, Codebook and Component analysis.
His research integrates issues of Salient and Sparse approximation in his study of Computer vision.
As a member of one scientific family, Matti Pietikäinen mostly works in the field of Feature extraction, focusing on Image texture and, on occasion, Gaussian blur, Gaussian noise and Image noise.
Between 2015 and 2021, his most popular works were:
- Deep Learning for Generic Object Detection: A Survey (461 citations)
- Median Robust Extended Local Binary Pattern for Texture Classification (200 citations)
- Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods (149 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Local binary patterns.
His studies link Machine learning with Artificial intelligence.
His research in Pattern recognition is mostly concerned with Texture filtering.
His Feature extraction study integrates concerns from other disciplines, such as Texture, Face hallucination, Texture compression, Image texture and Convolutional neural network.
His studies deal with areas such as Salient and Sparse approximation as well as Computer vision.
His Local binary patterns research is multidisciplinary, relying on both Vector quantization, Gaussian blur, Discriminative model, Gaussian noise and Robustness.
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