Personal Identification Using Different Biometrics : A Review

Personal Identification Using Different Biometrics : A Review

1.Santosh P. Shrikhande, 2.Hanumant S. Fadewar

1.Assistant Professor, School of Technology, SRTM, University, Sub-Centre, Latur

2.Assistant Professor, School of Computational Sciences SRTM, University, Nanded

Abstract Biometrics is the method of automatic identification of an individual by using their certain measurable physiological or behavioral characteristics like fingerprints, palm prints, hand geometry, iris, retinas, faces, hand veins, facial expressions, signatures, and voiceprints. Biometrics has overcome the problems of traditional verification methods like cards, tokens and Password or PINs. Biometric indicators have an edge over traditional security methods in that these attributes cannot be easily forgotten, stolen or shared; personally they have to go through the system. This paper reviews all biometrics and their various studies that have explored the technical and convenience issues and comparison between all the biometrics with an objective to provide insights on their reliability, performance, security, convenience and acceptance.

Keywords: Biometrics, verification, authentication

1. INTRODUCTION

   Now a days world is becoming very much insecure and hence people are looking towards the new type of security which is more secure, reliable and accurate. Biometric is one of them recent technology which provides reliability, security and accuracy [3]. Biometric is the technology of verifying people by using their measurable physiological or behavioral characteristics such as finger print, palm print, faces, retinas, iris, voice, and gait [4, 26]. Biometric authentication is highly reliable, because they provide a nontransferable method of identifying people and not just cards, badges or keys [2]. The main advantage of using biometrics is that human characteristics cannot be misplaced or forgotten like cards, password or PINs. Biometrics characteristics are classified into two categories.

   1. Physiological Characteristics

      The characteristics those are related to the human body or body shapes those are varies from person to person like Finger print, Palm print, Hand geometry, Hand veins, Face, Iris, Retinas and etc [1, 2, 26].

   2. Behavioral Characteristics

   The characteristics those are related to behavior of person while doing some actions such as Signatures, voice, Gestures, Gait, Keystrokes, Facial expressions and etc [1, 2, 26].

2. BIOMETRIC SYSTEMS

   Biometric systems are the automated systems can automatically recognize the person on the basis of their physiological and behavioral characteristics [4]. Biometric systems involve following units through which recognition is done which is shown in the following figure1.

   Figure 1: Biometric Recognition System

   1. Acquisition Unit

      In acquisition unit an image is captured by the sensing device such as camera. This is the important unit because the accuracy of entire system is totally depending on the quality of input image. So better the quality of image acquisition, better the image recognition [3].

   2. Preprocessing Unit

      In preprocessing unit the input image quality is enhanced by applying various preprocessing techniques for the better feature extraction purpose. Image preprocessing involves removing low frequency backgrounds, noise, normalizing the intensity of each pixel and image transformations [3, 5].

   3. Feature extraction Unit

      Introducing images to the computer system is called as feature extraction. In this unit image features are extracted those can be used to identify the person based on fact that each person has unique characteristics. For example finger print of person has the features like whorls, arches, loops, ridges, furrows and minutiae. There are many more features extraction techniques and algorithms are used to extract the features from biometric trait. The extracted features are represented as a vector called as features vector which is useful in matching unit [3, 5].

   4. Matching Unit

   In the Matching unit, the features of query image are compared with the template image features. If the query image features are matched with template image then recognition is successful. This process can be done in two

   steps – Verification stage or Identification stage. In verification stage, it has to verify the person who claims an identity. So his biometric features those were extracted are compared with the stored trait in the database. Actually this is a one to one matching process. But in the identification process the extracted features are compared with all the features those are stored in the database. So the identification process is a one to many matching process [3, 5].

3. BIOMETRICS TYPES

   Based on Physiological and behavioral Characteristics following are the biometric types [1] discussed in this paper.

   1. Finger Print Technology

      This is the oldest of all biometric technique and widely accepted by the people. Its applications are in forensic investigation, law enforcement, personal identification and end point security. Finger print is an impression of the ridges of all parts of the finger [2, 5]. The fingerprint image contain dark lines are called as ridges and bright lines are called as valleys. Ridges and valleys run parallel, sometimes they bifurcate or sometime joins in one or sometime they terminates. The points where the ridges are suddenly terminated or bifurcated or combined such points are called as minutiae points [21]. A good quality finger print containing around 25-80 numbers of minutiae point. Based on the ridge valley pattern, finger print matching techniques can be classified as minutiae based or correlation based. Minutiae based techniques attempts to match minutiae points of query image with template image and determine the total number of matched minutiae. Correlation based technique compares the global pattern of ridges and furrows to see if the ridges in the two finger prints are matching or not [2, 21].

   2. Face Recognition Technology

      Face recognition is the automated method of detecting and matching a face of person with existing database [2, 9]. Face recognition technologies are used in airports, multiplexes, malls and other public places to detect the presence of criminals, terrorist among the public crowd [6]. Every human face has unique and distinct landmarks and these landmarks are called as Nodal points. Human face has around 80 nodal points. In face features extraction, most of the techniques analyzes the relative position of the face, size of the face, shape and depth of the eyes, distance between the two eyes, size and shape of nose, nasal breadth, cheek boons and depth of jaw line of the persons face [3, 7, 21]. These features are stored in feature vector and used for verification and identification purpose in matching step of the face recognition system [2, 3]. Face recognition system uses different approaches for the recognition; Holistic approach method uses whole face image for feature extraction is commonly called appearance based strategy. The feature based method uses local features of the face such as nose, mouth, eyes, eyebrows and their relationship with each other. The Hybrid approach method uses combination of both holistic and feature based method for better performance [8]. There are two types of face recognition systems, first is 2-D system, which require more

      illumination to capture an image and supports minor variations in the face orientation. Hence it is very dfficult to recognize the face with expressions and orientation [6, 7]. Second is 3-D system, where range cameras are used to capture the 3-D view of faces. This system supports variations in faces orientation up to 90 degree and hence it is not so difficult to recognize the facial expression and orientation [6, 7, 8].

   3. Iris Recognition Technology

      Iris recognition system uses an iris of human eye which is unique in every individual [5]. Iris recognition is used in various areas of life such as airports, crime detection, business applications, banks, and personal identification in firms and industries. Iris recognition is becoming an important biometric as compare to other because iris is protected from external environment behind the cornea and the eyelid [2]. Iris is the annular colored ring between the pupil and sclera of the eye and structure of iris is fixed over a time. The gray level intensities of iris of two individuals differs from each other. This difference is found in between identical twins and even in between left and right eye of the same person [9]. Iris image is captured by the camera with proper illumination for the better quality of iris image. Preprocessing techniques are applied on iris image for better feature extraction purpose such as pupil and iris boundary, eyelid detection and its removal [9]. Feature extraction module extracts the most significant features of an iris for the verification and recognition purpose. Some of the feature extraction techniques of iris are based on the radius of iris, orientation of pupil, shape and size of the pupil, intensity values of pupil and ratio of average intensity values of the two pupils [9]. Matching module compares the features of input iris image with the stored template iris image features [2, 9].

   4. Voice Recognition Technology

      The voice recognition biometric uses the voice of human being for identification. Voice is a very basic means for communication amongst the people [12]. It is also called as speech recognition system. Speech recognition is a process of converting speech signals to the words by means of algorithms and hence it is the special form of signal processing [5]. Automatic speech recognition has a wide range of application in such a areas where human interface is not required such as automatic call processing in telephone network, telephone directory inquiry without operator help, ovens, refrigerators and washing machine, robotics, automated transcription, and air traffic control [11]. Speech recognition system is divided in four different categories based on the type of utterance they can identify [11, 12]. First is Isolated word, where each utterance should quiet on both sides of sample window hence it accepts single word or utterance at a time. Second is Connected word, where it will allow separate utterance to be run together minimum pause between them. Third is continues speech, it will allow the user to speak naturally continuous while computer determine the contents. Fourth is Spontaneous, where it will allow natural sounding and not rehearsed such as words like run together, ums and ahs. The different feature extraction techniques used in speech

      recognition are Spectral features such as band energies, formats, spectrum and Cepstral coefficient mainly speaker specific information due to vocal tract, Excitation source feature such as pitch and variation in pitch, Long term feature such as duration, information energy due to behavior feature [11].

   5. Finger Vein Recognition Technology

      One of the recent biometric recognition technology invented is vein recognition. Finger vein authentication biometric uses the vein pattern of individuals finger for their personal identification. Every individual has a different vascular vein pattern in his finger which circulates the blood towards heart [15, 20]. Finger vein authentication is a promising biometric for individual identification in terms of reliability, security and convenience that is why many more applications are using finger veins authentication [17]. This biometric is reliable, secure, accurate and efficient as compare to other biometrics because of following reasons [17, 20].

      • Veins pattern is hidden inside the body skin and hence invisible to human eyes so it is difficult to forge.

      • It is contactless hence it is hygienic and more acceptable by the people.

      • Finger veins pattern image can only be taken from live body hence dead body cannot be used for identification.

      • Finger veins pattern is different even among identical twins and remain constant through the adult years. Hence it provides high accuracy [20, 26].

      Finger vein authentication process is done in four steps. The finger vein image is captured using near infrared camera. When infrared light passes on the finger then hemoglobin in the blood veins absorbs the penetrated infrared light and reflects vein image as the darker lines [15, 17, 20]. In preprocessing stage various techniques are used for vein image pattern adjustment and enhancement. In feature extraction module the measurable characteristics, attributes are used for introducing finger vein image to the computer systems for their recognition. In matching module the extracted features of input image is compared with template image for the recognition [20, 26].

   6. Some other biometrics

   Some of the other biometrics based on individuals physiological and behavioral characteristics are discussed below.

   • Hand Geometry

     Hand geometry biometric uses hand measurement characteristics of human for recognition purpose based on the fact that every human has unique hand geometry [2]. Hand measurement characteristics are length, width, thickness of the fingers, aspect ratio of the palm or fingers, thickness of the hand, curvature and relative location of these features differentiate every human. This biometric provide less accuracy in recognition because hand shape changes as per the age but it has a great user acceptance due to its ease of use and cost [3, 23].

   • Retina Recognition

     Retina scan biometric uses retina of the human being for recognition purpose as every individual has a unique retina. The structure of the retina includes sensing tissues and image receptor elements called as cones and rods. These cons and rods accept light rays and send the electrical impulses to brain for forming an image. Retina is not directly being seen so infrared light is used to illuminate the retina. The blood veins in the retina absorb infrared light and produce an image where blood veins appears as a dark lines. This retina blood veins pattern is used for the recognition. This biometric is very much accurate in terms of reliability and accuracy due to its uniqueness but very poor in terms of hardware cost and user acceptance [3, 24].

   • Signature recognition

     Signature recognition technology recognizes the person based on their behavioral signature. The signature of an individual is taken by the special pen and writing pad which is connected to the computer. While doing signature this system extracts the information of signature features based on the behavioral characteristics like speed, overall size, directions, acceleration, length of stroke and their time duration. These extracted features are stored as a template which is useful for person recognition. The advantage of this system is that it is easy to use and widely accepted by the people but it is less secure and accurate in terms of recognition [2, 3].

   • Keystroke Recognition

     Keystroke recognition technology uses the behavior of person while typing on the keyboard based on the fact that each individual has unique behavior. This technology contains keyboard connected to the computer and user is suppose to type. When user starts typing it extracts the behavioral features like the way person types, cumulative typing speed, time that elapses between the strokes, time that eachkey is held down. These features are used to recognize the person uniquely. Keystroke recognition is widely used due its ease of use and low cost but it provides less accuracy in recognition [2, 25].

   • DNA Recognition

     DNA recognition biometric recognizes the person based on the DNA sample as every person has unique DNA pattern. DNA is made-up of the nucleotides and it can be taken from blood, semen, tissues, cells and urine of the human being. The DNA samples from above sources are used for the recognition of an individual. This biometric used in forensic for crime detection and also used to prove the blood relations with parents. This biometric provide a great accuracy and reliability in recognition but has a very less user acceptance due to the implementation cost [2, 3, 5].

4. EVALUATION OF BIOMETRICS

   Nowadays it is well accepted that biometrics will never produce an error free recognition results. Therefore for the better use of any biometric system it has to evaluate for their

   performance, reliability and security based on following parameters [3, 18, 20].

   1. Security

      Biometrics is used for the secure identification and hence Security is the most important factor in evaluating the biometric systems. The security is referred as how secure and stable the biometric trait against anti forgery and permanence [3].

      • Anti forgery

        The data input can be forged or illegally used the trait of person for authentication such as the finger print on the glass can be used for authentication [20].

      • Permanence

        How much this Biometric trait is stable and it does not change over a time hence continue to work without data updates over long periods of time. This is also called as Long Term Stability of trait.

   2. Accuracy and Reliability

      Accuracy and reliability are the very much important factors that need to be considered while evaluating any biometric authentication system. Reliability is the rate of dependability on biometric for recognition purpose. Accuracy is the rate of correctness in recognition and which is measured using following factors [3].

      • False Accept Rate (FAR)

        The probability of accepting an imposter individual as a valid individual is called as False Acceptance Rate (FAR). It measures the rate of invalid matches, Less the FAR rate, better the authentication accuracy and reliability [3, 19].

      • False Rejection Rate (FRR)

        The false rejection rate is the percentage of rejecting the valid individual who is genuine user to the system. Less the FRR rate, better the authentication accuracy and reliability [19].

      • Equal Error Rate (EER)

        Equal Error Rate is the percentage of both accepting and rejecting rate is equal. The EER is obtained by taking the point where FAR and FRR is having same value. The lower is the EER; higher accuracy is considered [19].

      • Failure to Enroll Rate (FER)

        The rate at which biometric system fails to enroll the captured input image in a system by the acquisition unit. This is called as Failure to Enroll Rate (FER) where input accepted by sensor is treated as invalid input [19].

   3. Practicality

   Practicality is the major parameter to be considered while designing and implementing the biometric recognition system. It is measured based on the following aspects [20].

   • Performance

     The Performance or the speed of the biometric authentication system is that how quickly it gives the response to the users and does verification and identification [3].

   • Convenience

     Convenience is referred as how easily and simply one can use the biometric trait and the biometric system for recognition. This is also called as the ease of use

     where user can easily use the biometric system for recognition. If the biometric system is more convenient and easy to use then user acceptance is more to that system [3, 20, 27].

   • Hardware Cost

     Cost of the biometric system affects performance and security of authentication. The hardware cost is the cost requires to designing and implementing the biometric recognition system. Low-cost biometric doesnt provide high level of security. An ideal biometric will be cost- effective, when it is capable to provide a relatively high level of security at a low cost [3].

   • Template Size

   Size of sensing device is depending on the size of biometric trait used for the recognition. The size required to store the biometric trait into the database is called as template [3, 27]. The template size is depending on the size and quality of input data that will be stored in to the database.

5. COMPARISON OF ALL BIOMETRICS

   The biometric systems are evaluated on the basis of above all factors and their comparison is done on the basis of evaluated performance factors. The ideal biometric is one which rarely rejects an authorized individual (low false rejection rate, FRR) and rarely accepts an unauthorized individual (low false acceptance rate, FAR). The comparison of major biometrics based on the factors such as anti forgery, accuracy, reliability, long term stability, error rate, ease of use, user acceptance and hardware cost is shown in the following Table 1. [3, 20, 27].

   Table 1: Biometrics Comparison Chart

   Biometric Type	Anti Forgery	Accuracy	Reliabili ty	Long Term Stability	Error Rate	Ease of Use	User Acceptan ce	Hardwa re Cost	Errors Due to
   Finger Print	Low	High	High	High	1 in 500+	High	Medium	Low	Dryness, age, dirt.
   Face Prints	Medium	Medium	Medium	Medium	No data	Medium	Medium	Low	Age, glasses, hairs.
   Hand Geometry	Medium	Medium	Medium	Medium	1 in 500	High	Medium	High	Age, hand injury.
   Speech / Voice	Medium	Low	Low	Medium	1 in 50	High	High	Low	Noise, weather, cold.
   Iris Scan	Medium	Very High	High	High	1 in 1,31,000	Medium	Medium	High	Poor light, eye diseases.
   Retina Scan	High	Very High	High	High	1 in 10,000, ,000	Low	Medium	High	Poor lightning, eye diseases.
   Signature	Medium	Low	Low	Medium	1 in 50	High	Medium	Low	Change in signatures.
   Keystroke	Medium	Very Low	Low	Low	No data	High	High	Low	Hand injury, Tiredness.
   DNA	Medium	High	High	High	No data	Low	Low	High	None.
   Veins Pattern	High	High	High	High	No data	Medium	Medium	Medium	None.

   A perfect and ideal biometric system is one which would be very good at all the factors given in the above Table1.

6. CONCLUSIONS

This paper has discussed different biometric systems with their techniques for the feature extraction and authentication process. This paper has attempted to provide a comprehensive survey of all biometric recognition methods and their comparison for their reliability, performance, security, convenience, and user acceptance. This review has found that Iris and Retina recognition biometrics are more accurate in terms of recognition but they are poor in terms of practicality due to special hardwares cost and user acceptance due to the rays those are used for capturing iris scan are harmful to the eyes. Second is DNA recognition is also providing a great accuracy in recognition but it is very poor in practicality due to its practicality cost. Overall, the finger vein biometric is more secure and good in terms of anti forgery, accuracy and speed as well as average in terms of convenience and practicality. Hence Finger vein biometric is the promising, challenging and more acceptable biometric for personal identification. It is also found that unimodal biometric system is not enough for recognition accuracy purpose therefore multimodal biometrics can be used for the better security and accuracy results. We hope this review paper helps the researchers to choose their appropriate biometric trait for research among above biometrics or can go for multimodal biometrics.

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