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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training: Mastering Dlib: Advanced Facial Detection and Recognition Techniques with Python training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Mastering Dlib: Advanced Facial Detection and Recognition Techniques with Python training, Learni provides the following teaching resources:
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The assessment of skills acquired during the Training: Mastering Dlib: Advanced Facial Detection and Recognition Techniques with Python training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Presentation of Dlib, installation in various environments, overview of image processing with Python and OpenCV, first face detection pipeline, study of built-in models and their mathematical principles.
Using Dlib's landmark predictor, extraction and visualization of key points, face alignment correction, morphometric calculations, facial image processing and normalization, integration into interactive applications.
Creating and comparing facial embeddings, classification and identification, handling large databases, combating false positives and threshold adjustments, optimization on CPU/GPU, deployment of a complete facial recognition application, ethical and security considerations.
Target audience
Developers, data scientists, and AI engineers proficient in Python who wish to integrate facial detection and recognition solutions into their projects
Prerequisites
Strong proficiency in Python, basics in image processing, and knowledge of machine learning concepts
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