Emotion Detection and Recognition to Change the Game of Biometric Detection and Security

By Rohanish Chavan

The process of identifying human emotion including the different form of facial and verbal expressions constitutes emotion recognition. As the technology has advanced, it is now possible to detect and recognize these emotions. This immaculate detection has given rise to several possibilities in variety of fields such as security and surveillance. A pipeline for recognition, detection and understanding emotions can be set up using lines of code over the saved data base of images. The process of emotion detection and recognition includes several techniques such as computer version, signal processing, and signal processing. There can be several other methodologies that a user may partake in order to interpret emotions such as Hidden Markov Models, Bayesian network, and Gaussian Mixture models. These methodologies generally include cumulative functioning of multimodal video, texts and audio recordings of different human behaviors and emotions related to it. This helps in detecting and integrating the information from speech, facial expressions, gestures, and body language. A wide database can be created in order to distinguish between the emotions by classifying them into statistical methods, knowledge-based techniques, and hybrid approaches.

The segment of facial biometric can be divided in to three parts on the basis of their functioning. The primary segment consists of facial detection which include ability to detect the location of face in any input image or frame. Once the device scans the facial features of an individual it gives output in a form of bounded box coordinates of the detected face. After detecting the face, the next step is facial recognition. In this step, the program uses the detected facial features and runs across multiple faces that are stored in its database. If all the detected features match the features of the face stored in the database, the face of that individual is recognized. The advanced methodology of face recognition is detecting the emotions on the detected face. By running through complex programs and flow charts that are designed in the device, the difference in the face detected and the face recognized can be used to detect the emotions of an individual, that is whether the person is angry, sad, happy, surprised, disgusted, neutral, or frightened. However, this program is meant to detect simpler emotions and doesn’t work it the user is expressing complex emotions or a poker face.

The device that is used for facial detection and recognition follow simple protocols. These protocols include­

  • Detect the face.
  • Scan the features of the face.
  • Run it through the data base.
  • Match the features of the scanned face across the database.
  • Display the output if the face is identified or not.

Since the device follow these simple protocols it is comparatively easy to setup a face detection device to improve the security of any place. A user only needs to collect the information for the data base to run it across. Once the database is filled with the details of the authorized personnel, the facial recognition device is set to go. Now in order to further recognize the emotions of the personnel, the database may require a wide range of photos and videos that can be filled to the artificial intelligence program. The different photos and videos will help the AI in noticing the key features of the said personnel when they display any peculiar emotion. This will help the AI to categorize the emotions on the basis of seven categorizes such as angry, sad, happy, fear, surprise, neutral, and disgust.

Several enterprises have deemed this technology to be helpful across various fields and have made several ventures in order to develop and make advancements to this technology. After performing an in-depth research, Allied Market Research have estimated that the global emotion detection and recognition market would rise up to $33.9 billion by 2023, growing at a CAGR of 28.9% from 2017 to 2023. There are still several opportunities that need to be explored which will prove beneficial to the market. This market has a great potential and would rise exponentially as new technologies will be introduced in the near future.

There are several fields that could benefit from the advancements of emotion detection and recognition technology. Several marketing firms have taken initiatives in order to gain wide range of data regarding their customers’ needs. Sectors that require information about the customers emotional experience while purchasing different products could use the technology of detecting the emotions. This could play a vital role in connecting the brands with their audiences. They could collect the database of the emotions of their customers and could leverage this information to advertise their products efficiently. This would significantly enhance the customer experiences along with creating a lasting bond with the brands. Furthermore, the brands could offer suggestions to their customers depending on the data collected from their previous endeavors. Marketing firms can use this technology by integrating real time response to dynamically empower the customers and satisfy their needs. This technology can also be used for market research purposes. Sectors such as film industry can use this technology in order to determine what parts of the show the audience are enjoying. This would give the film makers and directors a general idea about creating a better play or movie. This technology can also help with interrogations or interviews, where the body language and mood of the person being interrogated can be determined. This would help in providing the information of how truthful that person is while answering the questions.

Rohanish Chavan is a content writer, working on a large marketing and sales platform that helps firms attract visitors and close customers. Proficient monitoring and evaluating search results and search performance in order to write market reports and editorials sums up his job description. He keeps his technical skills and knowledge up to date to optimize the social media updates and industry changes. In his free time, he also writes short stories, poems, and blogs in order to cultivate his writing prowess.

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