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by Smruthi Raj Mohan, Neha Setia, Manjula Hosurmath | Published January 9, 2019
Artificial intelligenceData scienceDeep learningMachine learningObject StorageVisual recognition
Facial recognition is considered to be one of the most promising applications in the field of image analysis. However, a robust facial recognition application must recognize an identity despite the many variations in appearance that the face might have in a scene. To increase the accuracy, you can use instances where a face detection algorithm fails and append a machine learning Object Detection model by using Tensorflow to detect the failed cases and treat them as an object. In this code pattern, we’ll demonstrate a way to extend the face detection function of Watson Visual Recognition by treating images as an object, which is detected by a model, and appended to a face detection algorithm.
A robust face recognition program must recognize an identity despite the many variations in appearance that the face can have in a scene. So, how can you achieve increased accuracy in face detection? The border cases, where face detection algorithms tend to fail can be treated as an Object. Then, a model can identify these objects and append it to a face detection algorithm.
Using the example of an image in which a person’s face remains covered – for example, a cloth or cap over a person’s head – these faces are treated as a separate object, labeled as Covered. Next, the objects that are detected are appended to a face detection algorithm. This process increases the number of detections that are done by a face detection algorithm, and the accuracy of the prediction.
This code pattern uses Watson Visual Recognition, Watson Studio, and a Python notebook to demonstrate a way to detect covered faces.
Get the detailed steps in the readme file. These steps will explain how to:
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