CNN-Based Personal Identification System Using Resting State Electroencephalography

As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.

An ear recognition system based on local wavelet subband energy distribution

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.

HUBUNGAN PANJANG-BOBOT, FAKTOR KONDISI DAN KARAKTERISTIK BIOMETRIK IKAN LELE AFRIKA (Clarias gariepinus) ALBINO ASAL THAILAND

The African catfish (Clarias gariepinus) has been introduced several times to Indonesia, including from Thailand (Paiton strain). The breeding of Paiton strain resulted in albino individuals. The present study aimed to investigate the length-weight relationship, condition factor and biometric characteristic of those albinos. Five pairs of each albino and normal coloured Paiton African catfish were artificially bred. Larvae and juveniles from each pair were reared one month of larval rearing phase, one month of nursery phase and two months of grow-out phase, then the total length and body weight were measured for length-weight relationship and condition factor analysis, finally the biometric was characterized. The present study revealed that the relationship between total length (L) and body weight (W) of the Paiton African catfish was positive allometric (W = 0.0038L3.23 in the albino and W = 0.0027L3.27 in the normal coloured one). The albino Paiton African catfish was more rotund (condition factor of 0.79±0.07) than the normal colored one (condition factor of 0.68±0.06). Biometrically, the albino Paiton African catfish has a bigger head portion and fewer dorsal and anal fin rays than those of the normal coloured one. Despite the colour difference, the albino and normal coloured Paiton African catfish were morphologically different.

Two-Factor Decryption: A Better Way to Protect Data Security and Privacy

Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, we introduce a notion called two-factor decryption (TFD). Informally speaking, a TFD scheme is a variant of the public-key encryption (PKE) scheme. In a TFD scheme, messages are encrypted under public keys as that in a standard PKE scheme, but both private keys (i.e. the first factor) and biometric inputs (i.e. the second factor) are required to decrypt the ciphertexts and obtain the underlying plaintexts. We first describe a framework of TFD, and then define a formal security model for TFD. Thereafter, we present a generic construction on TFD based on the cryptographic primitives of linear sketch and functional encryption (FE) with certain properties and analyse its security. In addition, we give instantiations of TFD by applying concrete FE schemes into the generic construction and show their applications.

Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems

Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.

A SURVEY ON FINGERPRINT PATTERN RECOGNITION

In this advance technical time, we all need accuracy to any security system. Among all security system, biometric recognition process is very popular in that time. Not only security purpose, identification is the main cause of using biometric characteristic. A pin, password combination is not enough to secure all things because that’s tracking is possible, but a person biometric characteristic is unique, so it is near to impossible to by-pass. In this paper, we discuss about the fingerprint types such as arch, loop, and whorl. We also discuss how the fingerprint will be recognized; however, where this technique is used in very large scale and what is the future scope of this technique, we discuss what improvement is needed in future.

KARAKTERISASI BIOMETRIK IKAN LELE DUMBO PERBANDINGAN DENGAN IKAN LELE AFRIKA (Clarias gariepinus BURCHELL, 1822)

Lele Dumbo was used to be a superior clariid catfish ardely cultured in Indonesia. Despite its aquaculture success, there was uncertainty about its taxonomic identity, whether it belongs to an African catfish (Clarias gariepinus Burchell 1822) or a hybrid resulted from a hybridization between African catfish C. gariepinus and an Asian catfish C. fuscus. Though lele Dumbo was no longer popular, the genetic improvement program has successfully developed lele Sangkuriang strain, and that have recently been extensively cultivated in Indonesia. As a lele Dumbo strain, the identity of lele Sangkuriang is also uncertain, thus need to be verified. The present study aimed to investigate the similarity of lele Dumbo through morphometric and meristic characterizations using samples of lele Sangkuriang (collected from BBPBAT Sukabumi, BPBAT Cijengkol and PT STP) compared to those of African catfish C. gariepinus introduced from Thailand and Kenya. The characterizations were carried out through measurement of 20 standard morphometric characters and five meristic characters the data obtained were then analyzed using principal component analysis. The results suggested that the values of morphometric and meristic characters of all three samples of lele Sangkuriang were not different from those of African catfish C. gariepinus. Likewise, the results of principal component analysis performed on morphometric and meristic characters also revealed that morphometric and meristic characteristics of all three samples of lele Sangkuriang were not different from those of African catfish C. gariepinus. Those results revealed that biometric characteristic of both lele Dumbo and African catfish C. gariepinus was not different, thus they seem belong to the same species.

Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels

Finger Texture (FT) is one of the most recent attractive biometric characteristic. Itrefers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. This inspired the insight of applying a combination between the FT features that acquired by utilizing two various spectrum lightings in order to attain high personal recognitions. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed and examined for an FT verification system. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance were recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%.