What is he best known for?
The fields of study he is best known for:
- Ecology
- Artificial intelligence
- Computer vision
Ecology, Spanish imperial eagle, Artificial intelligence, National park and Biometrics are his primary areas of study.
His study ties his expertise on Biological dispersal together with the subject of Ecology.
His Spanish imperial eagle research integrates issues from Conservation science, Threatened species, Archaeology and Predation.
The study incorporates disciplines such as Speech recognition, Computer vision and Pattern recognition in addition to Artificial intelligence.
His research integrates issues of Trophic level, Environmental protection, Reproduction, Environmental chemistry and Nature reserve in his study of National park.
In general Biometrics, his work in Signature recognition is often linked to Perspective, Witness and Multitude linking many areas of study.
His most cited work include:
- The mine tailing accident in Aznalcollar. (343 citations)
- Offline geometric parameters for automatic signature verification using fixed-point arithmetic (261 citations)
- Density‐Dependent Fecundity by Habitat Heterogeneity in an Increasing Population of Spanish Imperial Eagles (192 citations)
What are the main themes of his work throughout his whole career to date?
Miguel Ferrer mostly deals with Artificial intelligence, Ecology, Pattern recognition, Biometrics and Computer vision.
In his study, which falls under the umbrella issue of Artificial intelligence, Handwriting is strongly linked to Speech recognition.
His Ecology research includes elements of Zoology, Biological dispersal and Fecundity.
His Zoology research is multidisciplinary, incorporating perspectives in Pygoscelis and Blood chemistry.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Facial recognition system and Contextual image classification.
As part of his studies on Biometrics, Miguel Ferrer often connects relevant areas like Identification.
He most often published in these fields:
- Artificial intelligence (32.94%)
- Ecology (26.73%)
- Pattern recognition (21.48%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (32.94%)
- Ecology (26.73%)
- Biometrics (16.71%)
In recent papers he was focusing on the following fields of study:
Miguel Ferrer focuses on Artificial intelligence, Ecology, Biometrics, Pattern recognition and Handwriting.
His Artificial intelligence research is mostly focused on the topic Feature extraction.
His study in Ecology is interdisciplinary in nature, drawing from both Zoology, Biological dispersal and Natural.
His work deals with themes such as Spoofing attack, Data mining, Sclera and Data science, which intersect with Biometrics.
In general Pattern recognition study, his work on Signature recognition and Support vector machine often relates to the realm of Off line and Line, thereby connecting several areas of interest.
His Handwriting study integrates concerns from other disciplines, such as Dynamic time warping, Robotics, Set, Algorithm and Bengali.
Between 2015 and 2021, his most popular works were:
- A Perspective Analysis of Handwritten Signature Technology (98 citations)
- Dynamic Signature Verification System Based on One Real Signature. (64 citations)
- A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis (43 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Artificial intelligence
- Statistics
Miguel Ferrer mainly focuses on Biometrics, Artificial intelligence, Ecology, Handwriting and Pattern recognition.
His Biometrics research is multidisciplinary, incorporating elements of Segmentation and Image segmentation.
His Artificial intelligence study incorporates themes from Equivalence and Natural language processing.
Ecology and Zoology are frequently intertwined in his study.
Miguel Ferrer interconnects Stability, Speech recognition and Similarity in the investigation of issues within Handwriting.
His Pattern recognition study which covers Machine learning that intersects with Feature extraction, Scale-space segmentation and Segmentation-based object categorization.
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