Job Description
The position involves designing advanced systems to detect and prevent spoofing attacks during biometric authentication processes. This requires utilizing machine learning and deep learning techniques to create models capable of distinguishing between genuine human interactions and fraudulent attempts.
This includes analyzing facial features, eye movements, and other physiological indicators. The successful candidate will develop and optimize deep learning models specifically for liveness detection, selecting appropriate algorithms and conducting experiments to enhance accuracy and reliability.
A key aspect of the role is identifying and extracting features from biometric data that are crucial for detecting spoofing attempts. This includes texture analysis, motion-based detection, and 3D depth analysis.
The selected individual will collaborate with data scientists to collect, clean, and preprocess large datasets required for training liveness detection models. They will also implement machine learning algorithms capable of processing real-time biometric data to detect inconsistencies indicative of spoofing attempts.
Required Skills and Qualifications
* Expertise in machine learning frameworks such as TensorFlow, Keras, or PyTorch.
* Proficiency in programming languages such as Python, Java, or R.
* Strong analytical skills with a solid grasp of statistics, probability theory, and data analysis techniques.
* Ability to work effectively with cross-functional teams.
* Experience in similar roles focusing on anti-spoofing or biometric security systems.
Benefits
* We offer a comprehensive benefits package, including discounted health plan rates and optical assistance.
* Life assurance and income protection.
* Option to buy additional annual leave days.
* Employee Assistance Program.
* Flexible working arrangements.
* Benefits for you and your family.
* Access to learning and development resources.