Improve AI-ML-based facial recognition application accuracy by validation through diverse real data sets using a user testing model. Conducting multiple face recognition trials in different environments and backgrounds to train the AI-based app and validate how it determines the behavior of the user.
In an era of AI and machine learning, artificial intelligence is rapidly replacing human intervention in multiple situations. Increasingly, they are providing the first line of customer service to the end users. One of which is facial recognition technology, which is used to identify people on a static or moving image.
Face recognition technology is an AI-ML-based software application that is designed to recognize a person’s identity by scanning their face. It enables AI-based apps to identify and recognize human faces from images or videos, in order to train them to recognize and identify the right faces.
Generally, The science of face recognition involves understanding biological systems for recognizing faces. This is a biometric technique that compares and analyzes patterns based on a person’s “facial contours,” i.e., it identifies or confirms a person’s identity based on their facial features.
How does it work?
- Detects the face
- Analyzes the face
- Converts the data from image
- Recognises and finds the match based on the datasets in the database
Benefits:
- Efficient identification in real-time
- Convenient and fast
- Accuracy is high
- Security is strong
- Reduced human touchpoints like fingerprints.
Where is it used?
- To detect fraud, In Security systems for criminal detection and forensic surveillance systems.
- Banking and healthcare domain
- Airports for biometrics
- Face recognition for lock screens and biometric identification in mobile phones
- Medical tools for treatment
- Hotel check-in
- Event entrances
Understanding the Scope of AI app Testing – Maturity Assessment
Unlike typical software application testing, Ai app testing is not just about assessing the functionality of the app, but more about assessing the maturity of the Ai algorithm via its responses to various facial detections and identification.
Any AI based application testing plan needs to go through the following process of assessments:
- Training your AI solution with an accurate training data set: It is important to feed high-quality and accurate data to train the AI. This is done to make the algorithm understand how to apply the concept to take input, assess and share the expected result.
- Functional Accuracy:
- Creating a sufficient volume of data set: Based on the defined recognition logic, create enough data with all variants of gender, ethnicity, facial features, color, skin, eyes, nose, ears, neck, mouth, etc, jewelry, and clothes.
- Unbiased data set: The data used for training must contain variants as described above, in order for the algorithm to recognize any face scanned without bias. The algorithm is well-trained to address fairness, color, discrimination, etc.
- Non-deterministic data set: Different outputs may be produced when the same face is recognised during various runs. For example: The behavior, for instance, will differ from earlier runs in the output.
- Attributes recognition and explainability: Based on the face recognition input, the algorithm will liberate the demographic information like age, gender, ethnicity, etc to understand and identify the impact.
- Continuous learning and training: Because the algorithm is trained with every possible recognition, the challenge in this situation is never-ending and thus must be recognised, taught, and applied on a regular basis. This in turn leads to achieving improved identification and recognition, more accuracy, saves time, and improves performance on the whole.
Key Challenges in AI-ML based face recognition application testing:
- Accuracy:
- Illumination: The changes in light conditions
- Pose: Movement of head or angle being captured depends on the user
- Expression recognition: To recognise identity and emotions. Varied circumstances result in different moods, which cause people to express different emotions and, ultimately, modify their facial expressions.
2. Similarity of faces and ethnicities: Poorer recognition of other-racist faces than own. In real-world settings, this problem contributes to difficulties in social interaction, inaccurate eyewitness identification, and face-to-face match issues in security.
3. UX and compatibility on different platforms: The user experience caused by the incompatibility of AI-based apps may vary from platform to platform. User experience can be provided to a certain extent, but platform compatibility is a difficult undertaking.
4. Performance of the application, when there is an increased number of detections: There is an impact on the performance, when multiple users are involved.
