Aleksei Alekseev

Leading the Face Recognition team
Full SOTA face recognition pipeline (from classical CV to deep learning):
Face detection, alignment, generation, quality, embedding extraction, data scrapping and processing, training on large-scale datasets (custom high performance utilities, Multi-GPU, model- and data-parallel training) etc.
Achievements and challenges

• 1st place NIST FRVT April, 2021

• 1st place NIST FRVT September, 2023 - 2024

• Gold medal in Happywhale - Whale and Dolphin Identification challenge

LLM stack

Development of AI agents utilizing Chain of Thought (CoT), structured outputs, functional calling, Retrieval-Augmented Generation (RAG), etc.

Work experience
0) Classical computer vision: edge detection, viola-jones algorithm, keypoints detestion (SURF, etc), segmentation (GrabCut), tracking, noise removal, watermark removal, 3D scan application, based on two fixed web cameras.
1) Image classification. Painting classification (style, genre), Type of room, etc.
2) Liveness (anti-spoofing), commercial product. Also took 2nd place in a face anti-spoofing challenge
3) Several projects with Microsoft HoloLens, also gesture recognition with MS Kinect
Main publications
1) Automatic coloring of grayscale images based on intelligent scene analysis (https://link.springer.com/article/10.1134/S1054661815010022)
2) Context-Sensitive Image Analysis for Coloring Nature Images
(https://link.springer.com/chapter/10.1007/978-3-319-33816-3_14)