Face and Asset Recognition Testing in the Real World

The era of traveling the world by just scanning your face is fast approaching. Scan your face to pay, enter the subway, punch ID cards, and clock in and out of work! An emerging trend in computer vision and pattern recognition, facial recognition can make our lives easier and more flexible in several ways and is one of the most widely used computer vision applications today.

What is Facial Recognition?

Facial recognition is a technology-based method of identifying a human face. Through biometrics, it maps facial features from an image or video. Given any image, the goal of facial recognition software would be to determine whether there are any faces and return the bounding box of each detected face.

Testing Facial Recognition Software

Testing or the digital assurance process of facial recognition software is always fascinating since it encourages the tester to grow at every step. Apart from face detection, the software can assign specific emotions to every identified expression.

Facial recognition software performs the following steps:

Recognition: Matches a face from a database of faces

Verification: Verifies the identity of a person by their face

Detection: Detects the presence of faces in an image

Project Experience in Face and Asset Detection

Face and asset detection can be done using a multipurpose machine learning framework called TensorFlow. Functioning like an open-source library, TensorFlow trains huge models across clusters in the cloud; it can also run models locally on an embedded system like your phone/IoT device. It provides pre-trained images, which can help you make your own face detection software.

Once we receive a software which is ready for face and asset detection, we can collect few sample images which the developer has used for developing the face and asset detection software or it can be directly taken from tensor flow model.

• Start the application by providing sample images
• Remember that not every application is good at detecting a face or asset with 100% accuracy each time. Following the developer’s guidelines and indicating in the image how accurate the detection is would be highly advisable.
• You may have been exposed to real-time mobile applications that estimate age as a user. These apps are also evaluated using pre-loaded or ‘trained’ images.

In addition to face detection, we also have asset detection, which we refer to as milestones. Milestones are the assets provided every few kilometres or so for vehicles, especially along the railway. These assets contain multiple numbers or symbols, and the assets are monitored by comparing the identified symbol or number with the trained pictures.

You might be interested in: Deepfakes: Your Face or Voice can be Swapped or Hacked

Tools used for Face and Asset Detection Testing

TensorFlow Tool- open-source library developed by Google primarily for deep learning applications. Apart from tools, deep learning skills will also be required, i.e., we will use the deep learning pre-trained model to detect faces from different angles. 

Disadvantages of Facial Recognition

While facial recognition has definite advantages, it also has its downsides:

• Greater threat to individual and societal privacy 
• Infringement of personal freedoms 
• Violation of personal rights  
• Data vulnerabilities created
• Opportunities for fraudulence  
• Technical glitches   
• Incrimination against innocent people