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Python Image Recognition

Python Image Recognition

     

Understanding Python for Image Recognition

Python is a high-level coding language that enables the user to write software with a high degree of abstraction. This allows the user to keep a distance from the details of the computers, thus increasing the flexibility of the developed software. Indeed, the Python code can easily run on Windows PC, Linux or Mac. With the same simplicity, Python allows you to interact with very advances software—libraries—to do any kind of tasks. For example, there are libraries to open and manipulate images or to execute complex mathematical operations.

How Python Image recognition works

When using Python for Image Recognition, there are usually three phases to go through. The first phase is commonly called pre-processing and consists in taking the image you want to recognize and converting it into the right format. For example, one might want to change the size or cutting out a specific part of it. In the second phase, the image is sent to the mathematical model that does the recognition itself. In the third phase, usually called post-processing, the results of the model need to be interpreted. For example, a model that has three output to recognize dogs, cats and mugs will probably return a number for each of those categories. Therefore, you need to give a meaning to these numbers. For example, the highest number could be the category recognized into the image.

Given the Python flexibility, most of the very complex tools to generate mathematical models for image recognition such as Tensorflow or Pytorch are easily accessible. Hence, these complex libraries will do all the computations to modify the model parameters to reduce the errors made during recognition—a procedure called training. Most importantly, they will take advantage of the full power of the computer that has been used.

When speaking about human faces, there is a subtle difference in the terminology. Usually, it is called “detection”, when the model provides the exact coordinates of a human face in the image. Likewise, “recognition” is the operation of giving a specific name to the detected face. For example, the model can recognize a famous actor in a picture.

How Image recognition using Python is applied in business

Image recognition systems are widely used today. In the security field, they are used to recognize faces of criminals using surveillance systems. Facial authentication for unlocking phones is currently a big thing and major companies like Apple are leveraging this innovation to their benefit. These image recognition systems are also applied in customer service in banks, through KYC programs, to provide personalized services and enhance safety. They have also benefited insurance underwriting whereby the firms match photo ID proof with the face of the person using Face Match. Image recognition systems are a great milestone in computer science and Python greatly contributed to the increase of their impact into society thanks to its flexibility and abstraction.

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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.