Summary

What you'll do

1. Face detection algorithm development and optimization: Responsible for designing and developing high-precision face detection algorithms that can quickly and accurately detect the face position in different scenarios (such as indoors and outdoors, different lighting conditions, complex backgrounds, etc.). Continuously optimize algorithm performance, improve detection speed and accuracy, and reduce false detection and missed detection rates to meet the needs of fast face positioning in KYC business.
2. Face recognition algorithm innovation and application: In-depth research on face recognition technology, develop face recognition models based on deep learning, and improve the model's robustness to different postures, expressions, and age changes. By optimizing feature extraction and matching algorithms, improve the accuracy and efficiency of face recognition, ensure accurate recognition in large-scale face libraries, and provide reliable technical support for KYC identity verification.
3. Face anti-counterfeiting algorithm design and improvement: Focus on the research and development of face anti-counterfeiting algorithms, and comprehensively use a variety of technologies (such as liveness detection, texture analysis, infrared imaging, etc.) to effectively distinguish real faces from various counterfeiting methods (such as photos, video remakes, masks, etc.). Keep up with the latest counterfeiting technology, update and optimize anti-counterfeiting algorithms in a timely manner, and ensure the security of the KYC process.
4. Algorithm integration and system optimization: Integrate face detection, recognition and anti-counterfeiting algorithms into the KYC system, and effectively integrate with other modules (such as data collection, identity information comparison, etc.). With the help of image processing and computer vision technology, optimize the performance of the entire system to ensure the stability and efficiency of the algorithm in practical applications.
5. Cross-departmental collaboration and business support: Work closely with product, development, testing and other departments to deeply understand the KYC business process and needs, and seamlessly integrate face recognition technology into product functions. According to business feedback, timely adjust and optimize the algorithm to provide strong technical support for the smooth development of KYC business and enhance the competitiveness of products in the market.

What you'll need

1. Master degree or above, computer science, pattern recognition, image processing, artificial intelligence and other related majors.
2. Have a solid theoretical foundation in computer vision and deep learning, have a deep understanding of the technical principles of face detection, recognition and anti-counterfeiting, and be familiar with common face recognition models and related deep learning architectures (such as CNN, ResNet, Vit, etc.).
3. Have more than 3 years of work experience in face recognition related fields, and have practical project experience in KYC projects or similar identity verification scenarios are preferred.
4. Proficient in Python programming, proficient in at least one deep learning framework (such as PyTorch, TensorFlow), and able to independently complete the development, training and optimization of face recognition algorithm models.
5. Proficient in using image processing libraries such as OpenCV, have good algorithm implementation and code optimization capabilities, and can efficiently process image data.
6. Master data processing and analysis technology, be able to collect, clean, annotate and extract features of large-scale face data, and have experience in building and managing face databases.
7. Applicants with strong scientific research ability and innovative consciousness, who have published papers on computer vision and face recognition in well-known academic conferences or journals at home and abroad, or who have achieved excellent results in face recognition competitions, will be given priority.
8. Applicants with good teamwork spirit and communication skills, who can effectively cooperate with people from different professional backgrounds to jointly achieve project goals. Applicants who can quickly understand business needs and transform them into feasible technical solutions.

Job Responsibilities:

1. Face Detection Algorithm R&D and Optimization: Responsible for designing and developing high-precision face detection algorithms capable of quickly and accurately detecting face locations in various scenarios (e.g., indoors and outdoors, in varying lighting conditions, and against complex backgrounds). Continuously optimize algorithm performance to improve detection speed and accuracy, and reduce false positives and missed detections to meet the rapid face location requirements of KYC services.
2. Innovation and Application of Facial Recognition Algorithms: We will conduct in-depth research on facial recognition technology and develop a deep learning-based facial recognition model to enhance its robustness to different postures, expressions, and age variations. By optimizing feature extraction and matching algorithms, we will improve the accuracy and efficiency of facial recognition, ensure accurate recognition in large-scale facial databases, and provide reliable technical support for KYC authentication.
3. Design and Improvement of Facial Anti-Counterfeiting Algorithms: We focus on the research and development of facial anti-counterfeiting algorithms, integrating multiple technologies (such as liveness detection, texture analysis, and infrared imaging) to effectively distinguish real faces from various forgeries (such as photos, video remakes, and masks). We continuously monitor the latest forgery technologies, promptly update and optimize anti-counterfeiting algorithms, and ensure the security of the KYC process.
4. Algorithm integration and system optimization: Integrate facial detection, recognition, and anti-counterfeiting algorithms into the KYC system and effectively integrate them with other modules (such as data collection and identity information comparison). Leveraging image processing and computer vision technologies, we optimize the performance of the entire system and ensure the stability and efficiency of the algorithms in practical applications.
5. Cross-departmental collaboration and business support: We work closely with product, development, and testing departments to gain a deep understanding of KYC business processes and requirements, seamlessly integrating facial recognition technology into product functionality. Based on business feedback, we promptly adjust and optimize algorithms, providing strong technical support for the smooth implementation of KYC services and enhancing product competitiveness in the market.

Job requirements:

1. Master degree or above in computer science, pattern recognition, image processing, artificial intelligence and other related majors.
2. Possess a solid theoretical foundation in computer vision and deep learning, have an in-depth understanding of the technical principles of face detection, recognition, and anti-counterfeiting, and be familiar with common face recognition models and related deep learning architectures (such as CNN, ResNet, Vit, etc.).
3. Applicants with more than 3 years of experience in facial recognition related fields and practical project experience in KYC projects or similar identity verification scenarios are preferred.
4. Proficient in Python programming, proficient in at least one deep learning framework (such as PyTorch, TensorFlow), and able to independently complete the development, training and optimization of face recognition algorithm models.
5. Proficient in using image processing libraries such as OpenCV, with good algorithm implementation and code optimization capabilities, and able to efficiently process image data.
6. Master data processing and analysis techniques, be able to collect, clean, label and extract features of large-scale facial data, and have experience in building and managing facial databases.
7. Applicants with strong scientific research capabilities and innovative spirit, who have published papers on computer vision and face recognition in well-known domestic and international academic conferences or journals, or who have achieved outstanding results in face recognition competitions will be given priority.
8. Possess good teamwork and communication skills, and be able to effectively collaborate with people from different professional backgrounds to achieve project goals. Able to quickly understand business needs and translate them into feasible technical solutions.

Skills

• Communications Skills
• Development
• Python
• Software Engineering
• Team Collaboration