result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Verification function can also handle many faces in the face pairs. In this case, the most similar faces will be compared.

FACE RECOGNITION

Face Recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return list of pandas data frame as output. Meanwhile, facial embeddings of the facial database are stored in a pickle file to be searched faster in next time. Result is going to be the size of faces appearing in the source image. Besides, target images in the database can have many faces as well.

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisherface method. Facial image recognition Eigenface method is based on the reduction of facedimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is matlab software. Keywords: face detection, Eigen face, PCA, matlab Extension: There are vast number of applications from this face detection project, this project can be extended that the various parts in the face can be detect which are in various directions and shapes. 

Face Recognition Operations

The technology system may vary when it comes to facial recognition. Different software applies different methods and means to achieve face recognition. The stepwise method is as follows:

• Face Detection: To begin with, the camera will detect and recognize a face. The face can be best detected when the person is looking directly at the camera as it makes it easy for facial recognition. With the advancements in the technology, this is improved where the face can be detected with slight variation in their posture of face facing to the camera.
• Face Analysis: Then the photo of the face is captured and analyzed. Most facial recognition relies on 2D images rather than 3D because it is more convenient to match to the database. Facial recognition software will analyze the distance between your eyes or the shape of your cheekbones.  
• Image to Data Conversion: Now it is converted to a mathematical formula and these facial features become numbers. This numerical code is known a face print. The way every person has a unique fingerprint, in the same way, they have unique face print.
• Match Finding: Then the code is compared against a database of other face prints. This database has photos with identification that can be compared. The technology then identifies a match for your exact features in the provided database. It returns with the match and attached information such as name and addresses or it depends on the information saved in the database of an individual.
• Face Recognition Softwares

  Many renowned companies are constantly innovating and improvising to develop face recognition software that is foolproof and dependable. Some prominent software is being discussed below:  

  a. Deep Vision AI

  Deep Vision AI is a front runner company excelling in facial recognition software. The company owns the proprietorship of advanced computer vision technology that can understand images and videos automatically. It then turns the visual content into real-time analytics and provides very valuable insights.  

  Deep Vision AI provides a plug and plays platform to its users worldwide. The users are given real-time alerts and faster response based upon the analysis of camera streams through various AI-based modules. The product offers a highly accurate rate of identification of individuals on a watch list by continuous monitoring of target zones. The software is highly flexible that it can be connected to any existing camera system or can be deployed through the cloud.  

  At present, Deep Vision AI offers the best performance solution in the market supporting real-time processing at +15 streams per GPU.  

  Business intelligence gathering is helped by providing real-time data of customers, their frequency of visits, or enhancement of security and safety. Further, the output from the software can provide attributes like count, age, gender, etc that can enhance the understanding of consumer behaviour, changing preferences, shifts with time, and conditions that can guide future marketing efforts and strategies. The users also combine the face recognition capabilities with other AI-based features of Deep Vision AI like vehicle recognition to get more correlated data of the consumers.  

   

  The company complies with the international data protection laws and applies significant measures for a transparent and secure process of the data generated by its customers. Data privacy and ethics are taken care of.  

  The potential markets include cities, public venues, public transportation,

• b. SenseTime

  • SenseTime is a leading platform developer that has dedicated efforts to create solutions using the innovations in AI and big data analysis. The technology offered by SenseTime is multifunctional. The aspects of this technology are expanding and include the capabilities of facial recognition, image recognition, intelligent video analytics, autonomous driving, and medical image recognition. SenseTime software includes different subparts namely, SensePortrait-S, SensePortrait-D, and SenseFace.  
  • SensePortrait-S is a Static Face Recognition Server. It includes the functionality of face detection from an image source, extraction of features, extraction, and analysis of attributes, and target retrieval from a vast facial image data