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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2023

01.11.2023

Fusion of speech and handwritten signatures biometrics for person identification

verfasst von: Ahmad A. M. Abushariah, Mohammad A. M. Abushariah, Teddy Surya Gunawan, J. Chebil, Assal A. M. Alqudah, Hua-Nong Ting, Mumtaz Begum Peer Mustafa

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2023

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Abstract

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is able to outperform other single modality API systems.
Vorheriger Artikel A hybrid adaptive neuro-fuzzy approach for automatic spoken digit recognition
Nächster Artikel CI-Mix: cut instance mix for robust speaker verification

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Literatur
Abushariah, A. A. M., Gunawan, T. S., Chebil, J., & Abushariah, M. A. M. (2012a). Automatic person identification system using handwritten signatures. In 2012 international conference on computer and communication engineering (ICCCE) (pp. 560–565). IEEE.
Abushariah, A. A. M., Gunawan, T. S., Chebil, J., & Abushariah, M. A. M. (2012b). Voice based automatic person identification system using vector quantization. In 2012 international conference on computer and communication engineering (ICCCE) (pp. 549–554). IEEE.
Anton, S., Artem, T., Andrey, P., & Igor, K. (2020). Modification of VGG neural network architecture for unimodal and multimodal biometrics. In 2020 IEEE east-west design & test symposium (EWDTS) (pp. 1–4). IEEE.
Barbantan, I., Vidrighin, C., & Borca, R. (2009). An offline system for handwritten signature recognition. In 2009 IEEE5th international conference on intelligent computer communication and processing (pp. 3–10). IEEE.
Bhotto, M. Z. A., & Amin, M. R. (2004). Bengali text dependent speaker identification using mel-frequency cepstrum coefficient and vector quantization. In 3rd international conference on electrical & computer engineering (pp. 569–572). Dhaka, Bangladesh.
Biswas, S., Ahmad, S., & Molla, K. I. (2007). Speaker identification using cepstral based features and discrete hidden Markov model. In 2007 international conference on information and communication technology (pp. 303–306). IEEE.
Borra, S. R., Reddy, G. J., & Reddy, E. S. (2018). An efficient fingerprint identification using neural network and BAT algorithm. International Journal of Electrical & Computer Engineering (2088–8708), 8(2).
Chakroborty, S., Roy, A., Majumdar, S., & Saha, G. (2007). Capturing complementary information via reversed filter bank and parallel implementation with MFCC for improved text-independent speaker identification. In 2007 international conference on computing: Theory and applications (ICCTA'07) (pp. 463–467). IEEE.
Chaudhari, B. M., Barhate, A. A., & Bhole, A. A. (2009). Signature recognition using fuzzy min-max neural network. In 2009 international conference on control, automation, communication and energy conservation (pp. 1–7). IEEE.
Choudhary, S. K., & Naik, A. K. (2019). Multimodal biometric authentication with secured templates—A review. In 2019 3rd international conference on trends in electronics and informatics (ICOEI) (pp. 1062–1069). IEEE.
Das, M. T., & Dulger, L. C. (2007). Off-line signature verification with PSO-NN algorithm. In 2007 22nd international symposium on computer and information sciences (pp. 1–6). IEEE.
Deshmukh, A., Pawar, S., & Joshi, M. (2013). Feature level fusion of face and fingerprint modalities using Gabor filter bank. In 2013 IEEE international conference on signal processing, computing and control (ISPCC) (pp. 1–5). IEEE.
Diraco, G., Leone, A., & Siciliano, P. (2013). Human posture recognition with a time-of-flight 3D sensor for in-home applications. Expert Systems with Applications, 40(2), 744–751.CrossRef
Elias, M. S. (2009). Speaker recognition using enhance MFCC (Mel Frequency Cepstral Coofecient), Doctoral dissertation, Universiti Utara Malaysia.
Ghayoumi, M. (2015). A review of multimodal biometric systems: Fusion methods and their applications. In 2015 IEEE/ACIS 14th international conference on computer and information science (ICIS) (pp. 131–136). IEEE.
Goh, C., & Leon, K. (2009). Robust computer voice recognition using improved MFCC algorithm. In 2009 international conference on new trends in information and service science (pp. 835–840). IEEE.
Guesmi, H., Trichili, H., Alimi, A. M., & Solaiman, B. (2015). Novel biometric features fusion method based on possibility theory. In 2015 IEEE 14th international conference on cognitive informatics & cognitive computing (ICCI* CC) (pp. 418–425). IEEE.
Hossain, E., & Chetty, G. (2011). Multimodal face-gait fusion for biometric person authentication. In 2011 IFIP 9th international conference on embedded and ubiquitous computing (pp. 332–337). IEEE.
Htike, K. K., Khalifa, O. O., Ramli, H. A. M., & Abushariah, M. A. (2014). Human activity recognition for video surveillance using sequences of postures. In The 3rd international conference on e-technologies and networks for development (ICeND2014) (pp. 79–82). IEEE.
Huang, L., Yu, C., & Cao, X. (2018). Bimodal biometric person recognition by score fusion. In 2018 5th international conference on information science and control engineering (ICISCE) (pp. 1093–1097). IEEE.
Hussein, I. S., & Nordin, M. J. (2014). Palmprint verification using invariant moments based on wavelet transform. Journal of Computer Science, 10(8), 1389.CrossRef
Ismail, I. A., Ramadan, M. A., El Danf, T., & Samak, A. H. (2008). Automatic signature recognition and verification using principal components analysis. In 2008 5th international conference on computer graphics, imaging and visualisation (pp. 356–361). IEEE.
Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20.CrossRef
Jusman, Y., Cheok, N. S., & Hasikin, K. (2020). Performances of proposed normalization algorithm for iris recognition. International Journal of Advances in Intelligent Informatics, 6(2).
Kartik, P., Prasanna, S. M., & Prasad, R. V. (2008). Multimodal biometric person authentication system using speech and signature features. In TENCON 2008–2008 IEEE region 10 conference (pp. 1–6). IEEE.
Krawczyk, S. (2005). User authentication using on-line signature and speech. Michigan State University.
Krobba, A., Debyeche, M., & Amrouche, A. (2010). Evaluation of speaker identification system using GSMEFR speech data. In 5th international conference on design & technology of integrated systems in nanoscale era (pp. 1–5). IEEE.
McCabe, A., Trevathan, J., & Read, W. (2008). Neural network-based handwritten signature verification. Journal of Computers, 3, 9–22.CrossRef
Odeh, S. M., & Khalil, M. (2011). Off-line signature verification and recognition: Neural network approach. In 2011 international symposium on innovations in intelligent systems and applications (pp. 34–38). IEEE.
Ong, M. G. K., Connie, T., Jin, A. T. B., & Ling, D. N. C. (2003). A single-sensor hand geometry and palmprint verification system. In Proceedings of the 2003 ACM SIGMM workshop on biometrics methods and applications (pp. 100–106).
Patil, A. P., & Bhalke, D. G. (2016). Fusion of fingerprint, Palmprint and iris for person identification. In 2016 international conference on automatic control and dynamic optimization techniques (ICACDOT) (pp. 960–963). IEEE.
Prasad, S. M., Govindan, V. K., & Sathidevi, P. S. (2009). Bimodal personal recognition using hand images. In Proceedings of the international conference on advances in computing, communication and control (pp. 403–409).
Rana, H. K., Azam, M. S., Akhtar, M. R., Quinn, J. M., & Moni, M. A. (2019). A fast iris recognition system through optimum feature extraction. PeerJ Computer Science, 5, e184.CrossRef
Ranjan, R., Singh, S. K., Shukla, A., & Tiwari, R. (2010). Text-dependent multilingual speaker identification for Indian languages using artificial neural network. In 2010 3rd international conference on emerging trends in engineering and technology (pp. 632–635). IEEE.
Revett, K. (2008). Behavioral biometrics: A remote access approach. Wiley.CrossRef
Sahoo, S. K., Choubisa, T., & Prasanna, S. M. (2012). Multimodal biometric person authentication: A review. IETE Technical Review, 29(1), 54–75.CrossRef
Sufyanu, Z., Mohamad, F. S., & Ben-Musa, A. S. (2015). A proposed integrated human recognition for security reassurance. American Journal of Applied Sciences, 12(2), 155.CrossRef
Taouche, C., Batouche, M. C., Berkane, M., & Taleb-Ahmed, A. (2014). Multimodal biometric systems. In 2014 international conference on multimedia computing and systems (ICMCS) (pp. 301–308). IEEE.
Thepade, S. D., & Bhondave, R. K. (2015). Bimodal biometric identification with Palmprint and Iris traits using fractional coefficients of Walsh, Haar and Kekre transforms. In 2015 international conference on communication, information & computing technology (ICCICT) (pp. 1–4). IEEE.
Vasuhi, S., Vaidehi, V., Babu, N. N., & Treesa, T. M. (2010). An efficient multi-modal biometric person authentication system using fuzzy logic. In ICoAC 2010 (pp. 74–81). IEEE.
Wang, L., Minami, K., Yamamoto, K., & Nakagawa, S. (2010). Speaker identification by combining MFCC and phase information in noisy environments. In 2010 IEEE international conference on acoustics, speech and signal processing (pp. 4502–4505). IEEE.
Wen, J., Fang, B., Tang, Y., & Zhang, T. (2007). Offline signature verification: A new rotation invariant approach. In 2007 IEEE International conference on systems, man and cybernetics (pp. 3583–3587). IEEE.
Yamada, H., Ahn, J., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2020). Gait-based person identification using 3D LiDAR and long short-term memory deep networks. Advanced Robotics, 34(18), 1201–1211.CrossRef
Yantorno, R. E., Smolenski, B. Y., Iyer, A. N., & Shah, J. K. (2004). Usable speech detection using a context dependent Gaussian mixture model classifier. In 2004 IEEE international symposium on circuits and systems (IEEE Cat. No. 04CH37512) (Vol. 5, pp. V–V). IEEE.
Yee, C. S., & Ahmad, A. M. (2008). Malay language text-independent speaker verification using NN-MLP classifier with MFCC. In 2008 international conference on electronic design (pp. 1–5). IEEE.
Zulfiqar, A., Muhammad, A., & AM, M. E. (2009). A speaker identification system using MFCC features with VQ technique. In 2009 3rd international symposium on intelligent information technology application (Vol. 3, pp. 115–118). IEEE.
Metadaten
Titel
Fusion of speech and handwritten signatures biometrics for person identification
verfasst von
Ahmad A. M. Abushariah
Mohammad A. M. Abushariah
Teddy Surya Gunawan
J. Chebil
Assal A. M. Alqudah
Hua-Nong Ting
Mumtaz Begum Peer Mustafa
Publikationsdatum
01.11.2023
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2023
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-023-10052-x

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