We have studied the human authentication system using the time-variant channel measured with multi-input multi-output (MIMO) array. However, the system needs to authenticate the subject under restraint because it uses the positional relation between the subject and array, and this is not suitable for a daily living space. We propose the human authentication system without strict position restraint by using the bistatic MIMO radar. In this paper, we evaluate the robustness of the human authentication system for the subject's position, and the precision of the human radar to evaluate the practicability of proposed system. The experimental results showed that our human authentication technique successfully works without restraining the subject if the subject presents exactly at the pre-determined location, and the recognition rate of this case was 95%.
Developments in the internet of things (IoT) are driving a growing demand
for non-contact, easy-to-use and unconscious human authentication. Human
authentication is a technology for identifying an individual by comparing their
distinctive and quantifiable characteristics with a database. Authentications are
categorized into those based on physiological characteristics and those based
on behavioral characteristics. The former one exploits the shape of the human
body: common examples are fingerprints, hand veins and face shapes [
The authors have studied a human authentication system using a microwave [
We propose a human authentication system that uses human radar [
We evaluate here the robustness of this human authentication system [
Review of Authentication Using MIMO Array In this section, we briefly describe human authentication using the MIMO array for the better understanding of the following discussion. This study assumes the use of a MIMO array consisting of an Mr element array receiver and an Mt element array transmitter around the subject. Fig. 2 shows the positional relationship between the subject, array receiver and array transmitter. We measure the Mr Mt time-variant MIMO channel (1) 0 h11(t) . . . h1Mt (t) 1
... . . . ... hMr1(t) . . . hMrMt (t)
C , A for every registrant. hij is complex channel response from the j-th transmitter element to the i-th receiver element, and t represents the observation time. We store this channel as a reference channel HD(t, q) in the database. q represents the q-th registrant of the database. Next, we measure a test channel HT (t) for authentication under the same settings as the reference channels. The direct current (DC) component of the observed channel degrades the recognition performance. The DC component therefore needs to be excluded, so we define the DC-suppressed test channel and the reference channel respectively as HDe(t, q) and HT e(t, q).
The evaluation function ρ(p, q) which represents the sliding correlation between the test channel and the reference channel is defined as, ρ(p, q) =
PNT −1 PMt PiM=r1 HT e(tk, q)HDHe(p, q)
tk=0 j=1 qPNT −1 PMt PiM=r1 |HT e(tk, q)|2 PNT −1 PMt PiM=r1 |HDe(p, q)|2 tk=0 j=1 k=0 j=1
p = 1, 2, . . . , ND − NT + 1,
where NT and ND represent the number of snapshots of the test channel and
reference channel, and p is the number of the iteration applied in calculating
the sliding correlation. (·)H represents complex conjugate transposition. After
calculating the evaluation function (2) for every registrant, the maximum value
of ρmax(p, q) and the argument of the maximum value s is calculated. The
authentication system [
In [
Non-restraint Human Authentication Using a MIMO
Array
Human authentication [
This study groups the elements of the array into Marray antenna arrays and
uses Mt/Marray transmit antenna arrays and Mt/Marray receive array antennas.
The element separation in the array antennas is half a wavelength. For example,
in the case of Mt = Mr = 16 and Marray = 4, we assume 16 16 MIMO
array consisting of 4 transmit antennas that have 4 elements and 4 receive array
antenna that also have 4 elements. This antenna setting enables human radar [
During authentication [
In the registration step, the authentication system measures the reference channel and estimates the subject’s position. The pair of the reference channel and the subject’s position is stored in a database. In the authentication step, the system estimates the subject’s position using the test channel, and searches the reference channel with the same subject’s position in the database.
In an authentication system using human radar [
In this section, to evaluate the practicability of our proposed system, we
describe the experiments carried out to evaluate the positional robustness of the
system [
Robustness of the Human Authentication System dBm. In Fig. 2, a Single-Pole 64 Throw (SP64T) switch was used at the transmitting side. The CW signal was split to the receiver side as well since accurate synchronization between transmitting and receiving sides is required. At the receiver side, the received signals were input to a down-converter unit by way of a low-noise amplifier (LNA). The down-converted baseband signals (I1, Q1 I16, Q16) were digitized by a data-acquisition unit (DAQ). The rate for taking a snapshot of the MIMO channel was set to 100 Hz.
The reference channel measurement time was 10 seconds and the test channel measurement time was 3 seconds. The number of registrants was 4 and the reference channel of each registrant was measured at position A in Fig. 3. The test channel took 10 measurements for each position. Table 1 shows the individual and total recognition rate of each position. This illustrates the total recognition rate where the distance from position A of 5cm is degraded and lower than 50% in some positions. The recognition rate of position M and O is different in spite of the symmetrical antenna position. This is because the human body is asymmetric and the angle of reflection is different at each position. The asymmetry of human body seems to be one of the causes that the recognition rate of each subject is different in Table 1.
The relative error of the human radar system must therefore be smaller than 5cm. #$ %&’() #$ %&’(’) #$ %&’() #$ %&’(*) +, $ ) " ! # $ %
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Relative Error of Position Estimation
We evaluated the precision of the human radar system [
Fig. 4. CDF of relative error of the human radar [
This paper presented the performance of the non-restraint human authentication system, and its robustness against the positional error was investigated. The experimental results showed that our human authentication technique successfully works without restraining the subject if the subject presents exactly at the pre-determined location, and the recognition rate of this case was 95%.