Biometric authentication methods

1. B h u s a n C h e t t r i d i s c u s s e s d i ff e r e n t b i o me t r i c a u t h e n t i c a t i o n me t h o d s a n d h o w v o i c e b i o me t r i c s i s r e g a r d e d a s a f u t u r i s t i c a p p r o a c h Bhusan Chettri explains that every human being possess a unique and distinctive biological attributes that makes one person different from another. DNA, fingerprints, iris, facial pattern, and vocal apparatus are such unique characteristics that differ from person to person, and these are often utilized for the purpose of automatic person identification and verification using computer and AI algorithms. In this article, Bhusan Chettri takes a deeper look at how different biometric authentication methods are used, their limitations and advantages and how voice biometrics is different from them, and why the future of authentication should be based on voice will be discussed. Bhusan Chettri says, “many methods for biometric authentication are based on measuring the similarity of data points in the feature space. In other words, these methods extract relevant features from the data during both the training and testing phase of the system and basically measure the distance between the feature extracted from the test sample to that of the template created during the training step.”

2. DNA for authentication DNA stands for deoxyribonucleic acid, and it has been used for personal identification and authentication in different applications. One of the main advantages of using DNA as a means of biometric authentication is that the DNA of a person does not change whether he/she is alive or dead. It remains the same as a person transit through various phases of the life cycle — as an infant, childhood, adulthood, aging, and finally, death — the DNA does not change. Therefore, it is regarded as one of the most reliable forms of identification and person verification. However, practical usage and mass adoption of this method is not possible due to several shortcomings that are discussed next: (a) The whole process of analyzing DNA samples is time- consuming; (b) Ethical concerns. Protecting the privacy of people is important as different information extracted in the process can easily reveal the identity of individuals. Therefore, there must be safety measures in place to prevent spoofing; © Verifying the identity of twins: as twins usually share a similar genome it is hard to distinguish their identity using DNA; (d) Cost: the equipment involved in performing DNA analysis and maintenance of the infrastructure is expensive. Therefore, not every person or research group can afford to run biometric authentication using DNA unless their research is backed with good funding.

3. Fingerprints for authentication It is still one of the most widely adopted methods for personal identification. This is commonly used for verifying a person’s identity in passport control for border protection; digital identity; and financial services. Usually, all ten fingers are considered for the creation of the user’s fingerprint template. The fingerprint authentication method works in two stages. Enrollment: during this step, the system takes the user’s fingerprint of all ten fingers and creates the fingerprint template. For example, this step could be considered as someone entering a new country where he/she needs to go through the border control office. Here the person’s fingerprints are all recorded. This can be regarded as the creation of that user’s fingerprint template. Next time, when the person again enters the country, his fingerprints are again recorded. This time the system compares the stored fingerprint template with the new ones to verify that the person is the same and the system also pulls up all the records/history about the person from the last visit to that county. Unlike identity cards which can be spoofed easily, fingerprint authentication is difficult to spoof, and therefore it is often considered one of the most reliable forms of authentication methods. Furthermore, with such a method, a user is not required to remember a long password — which means there is no fear of forgetting a password. Thus, this method offers a simplified and convenient means of authentication. This method also has some limitations and drawbacks. Cost is one key factor. Implementation and maintenance of fingerprint

4. identification systems are costly — usually when it comes to an individual or small organization running such a system. Furthermore, like any other electronic device, it suffers an issue with power failure. This means that a constant power backup needs to be there in case of power failures to ensure the system is up and running. Another key issue with this method is that a significant amount of people with physical disabilities (loss of finger or hands) may be excluded from availing the facility of this method. IRIS for authentication An iris is a part of an eye behind the cornea that surrounds the pupil (an adjustable circular opening in the center of an eye). Every person has a unique iris pattern that does not change during their lifetime. As in other forms of identification, this methodology works in two stages. First, the template of iris for a user needs to be prepared. Then template matching is performed at test time (or deployment). It works by first locating the pupil position in an eye and then the location of the iris and eyelids are identified. Then unnecessary data such as eyelids, and eyelashes are removed through cropping and only the iris part is retained. From the retained data, relevant features are extracted, and a user template is created and stored in the database. At test time (during deployment) for a new person’s iris relevant features are extracted using the same methodology as used during the

5. training step. The template is then compared with the new features to find the degree of match between the two. It provides fast and accurate means of person authentication and works even when a person is wearing eyeglasses. It is possible to discriminate between twins using this method due to the uniqueness of iris patterns across twin children. With such contact-less methods for authentication, it makes it much more hygienic in using them in contrast to the fingerprint authentication method that involves physically touching the device. As the device operates on infrared rays, this form of recognition can work even in dark or night conditions. This method requires the use of high-quality sensors and an infrared light source and cannot operate on a normal camera. It may not work from a distance, and therefore requires the user to stare at the system’s scanner/camera from proximity which may not be comfortable for every user. Facial pattern for authentication Every person shares a unique face (with few exceptions of similarly looking faces or twins). Patterns extracted from an individual face are widely used for person authentication across different domains such as border control, law enforcement, personalized user experience, etc. Like other biometric systems (for example voice-based biometrics), to build facial recognition systems there are two major steps involved. Training and testing (deployment). The training step usually involves

6. extracting relevant features corresponding to a facial pattern of an individual and preparing a face template (or faceprint) using the extracted features. During deployment (while the system is up and running), it compares the image (either captured in a real-time or previously captured digital image) with the stored user’s face template to find the degree of similarity. If the degree of match is very high (as defined by the probability score returned by the authentication system) then the system allows the user access to services or passes control to the subsequent phases depending upon the application. For example, in the case of border and passport control, if the newly detected face matches one of the templates in the database of fraudsters, then the authorities might be alerted to take quick action on the matter. As the AI algorithms for facial recognition (dominated by deep learning) have advanced substantially in the past few years these systems can detect and verify persons with a high degree of accuracy even at night conditions (or even when a user is wearing a mask). For example, commercial phones such as iPhone X have Face ID that allows users to protect access using a face pattern. This means every time someone tries to access the phone, it prompts for a face match and allows access only to registered faces (in this case would be the phone owner). While such a method allows many benefits in terms of convenience and flexibility, it does have limitations and disadvantages. Privacy and security are some of the growing concerns about how these systems capture people’s images, store them in their database (or in the cloud), and how they are being used in their application pipeline. These data

7. could be hacked by scammers or hackers to steal someone’s identity or agencies may use them to track people without getting their consent. Furthermore, one of the major issues with such facial recognition technology is the racial biases (or discrimination) that such AI systems learn from the training data. The research on racial discrimination in face recognition technolog y by Alex Najibi found that the AI system trained to detect a person using their face was highly biased based on a person’s color. Their system performed poorly in recognizing black males and females while it showed a high degree of accuracy in recognizing white persons. Thus, one natural question that arises is how we can trust such a system that has learned bias and discrimination on its own. This further means that such AI systems are not faithful and cannot be trusted to be used in safety-critical applications. Because of such biases, in many parts of the US such as Boston, San Francisco, etc., police and local law enforcement agencies were banned from using facial recognition software. Alex Najibi is a 5th-year Ph.D. candidate studying bioengineering at Harvard University’s School of Engineering and Applied Sciences. Voice for authentication Voice (or speech) is the primary means of communication among human beings. Let us briefly talk about the human voice production mechanism. Broadly, there are three major parts (or let’s say steps) involved in speech/voice production by humans. These are breathing mechanisms through the lungs; the air produced from the lungs then passes through vocal folds that vibrate continuously to produce

8. different types of sounds; and finally, it passes through the vocal tract which is a kind of resonating system that helps produce different varieties of sounds. Different sounds, for example, vowels, consonants, etc. are produced based on how air generated from lungs interacts with vocal apparatus (vocal tract) including the position of tongue and articulation of lips (closure/opening). Usually, the length of the vocal tract for females is much shorter than those of males. This is one reason why female voices have a high pitch in contrast to male voices. Every person has a unique voice. Voice patterns are often used for the purpose of identity verification of an individual. Please check An overview of Voice Biometrics by Bhusan Chettri by Bhusan Chettri to learn the basics of voice biometrics. Bhusan also Explains the Usage & Challenges of Voice Authentication Systems. Although a person’s voice changes over time, today’s voice biometric systems take care of such implicit factors during their system development. Therefore, their accuracy remains almost the same even in conditions where the speaker is suffering from a sore throat and cold. Voice or speech conveys several levels of information. These are: Speech/voice: the message Emotion: the emotional state of the person Gender: male or female Language: which language is being spoken? Accent (demography): which part of the country/state? Speaker: who is speaking?       Based on the above, voice/speech technology has several applications.

9. Automatic Speech Recognition (ASR): this technology is used to extract with a high degree of accuracy the spoken words. In other words, an ASR system would output the text (transcript) from a given input spoken speech. Automatic Emotion Detection: this technology is used to extract the state of emotion of the speaker while he/she was speaking a particular phrase/word. This emotional state could take values such as happy, sad, angry, etc. Automatic Gender Detection: this technology is used to automatically extract gender information from a given speech/voice signal. It usually takes two values: male or female unless the system is trained to also consider neutral gender or transgender information. In such a case, the system will no longer remain a binary classifier as it has more than two output states representing gender information. Automatic Language Identification: given a speech/voice signal, this technology is used to identify which language is being spoken by the speaker. Automatic language identification systems are usually trained in a multi-class setting as there are more than two languages that need to be considered. With such automatic systems in place, it becomes easier, for example, for border control and law enforcement agencies to understand the language being spoken by a person (who has been detained for some reason and doesn’t speak English) and therefore arrange for someone who understands the foreign language to deal with the situation more proactively.

10. Automatic Accent Identification: given a speech/voice signal, this technology is used to automatically detect/identify the accent used by the person during a conversation. With such information, it is often easier to also identify the demographic information of the person. For example, the accent used by an Englishmen from Yorkshire is completely different from an Englishmen in London. Automatic speaker Identification: Automatic speaker verification or automatic speaker identification technology aims at verifying/identifying the identity of a claimed speaker from a given speech signal. This post on speaker recognition and AI of voice authentication systems provides a good summary of the technology. The use of Voice technology is increasing day by day because of its flexibility and simplicity in operating and voice/speech being the very base of how humans communicate. Such technology gives users the freedom to do things by simply giving a command: a voice speaking from a distance to a machine. For example, speaking to a digital assistant e.g., Cortana or Alexa to play a song called Last Kiss by Pearl Jam while laying down on the bed. This is possible because of the integration of technologies — such as ASR, Natural Language Processing, Natural Language Understanding, and Text-to- Speech — into one engine (running on the cloud) that runs the functioning of a digital assistant. As evident from these products (Microsoft’s Cortana, Apple’s Siri, Google’s Assistant, and Amazon’s Alexa) there is a huge investment and development by Tech Giants in AI and machine learning for voice/speech technology with an aim to enhance user’s degree of interaction with the technology by simply using their voice. and challenges Apart from these top Tech Giants, Voice Technology has been adopted across numerous industries/sectors. For example, voice biometrics are

11. widely used in Banks, Insurance companies for personal authentication; they are used to enhance personal user experience; they are also used for surveillance and forensic applications. Despite the success brought by recent advancements in AI algorithms for voice authentication technology, like any other biometric technology, they are not 100% secure. They can be manipulated to gain illegitimate access to the biometric system of some other registered users and tamper with their data and perform illegal activities. Bhusan chettri recently explained security of voice biometrics and voice authentication and spoofing attacks . To counter such spoofing attacks, Dr. Bhusan Chettri who earned his Ph.D. in Voice Technology and AI (analysis and design of countermeasures for voice spoofing attacks using AI and Machine learning) from Queen Mary University of London has been working hard to make significant research contributions in the field (see google scholar ), raising awareness of the problem, issues about how biases in training data impact decision of anti- spoofing systems and have also focused on explaining the predictions of such systems so as to build better, reliable and trustworthy systems for spoofing detection. No matter what, because of ease in operating conditions using voice, voice biometrics has more potential for user authentication. Combined with other biometrics such as iris and facial recognition, the ensemble system for person authentication could bring more accuracy in detecting and discriminating a real person from fraudsters.

12. Initially posted by: https://ventsmagazine.com/2022/08/31/different- biometric-authentication-methods-and-how-voice-biometrics-is- regarded-as-a-futuristic-approach/ Relevant Links: ISSUU DEEPAI ORCID GITHUB QUORA