i-manager Publications

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

Most Cited

Volume 2 Issue 1 March - May 2015

Research Paper

Face Detection and Recognition Based on Facial Features and Key Points Matching

Basava Raju* , K. Y. Rama Devi, P. V. Kumar*

* Research Scholar, Jawaharlal Nehru Technological University, Kakinada, AP, India.

HOD, Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad.

* Professor, Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana.

Raju, K. B., Ramadevi, Y., and Kumar, P. V. (2015). Face Detection and Recognition Based on Facial Features and Key Points Matching. i-manager’s Journal on Pattern Recognition, 2(1),1-9. https://doi.org/10.26634/jpr.2.1.3370

Abstract

Face Recognition has benefitted greatly from the many databases that have been produced to study it. Most of these databases have been created under controlled conditions to facilitate the study of specific parameters on the Face Recognition problem. These parameters include such variables as Position, Pose, Lighting, Expression, Background, Camera Quality, Occlusion, Age, and Gender. While there are many applications for Face Recognition Technology in which one can control the parameters of Image Acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This paper is provided as an aid in studying the latter, unconstrained, face recognition problem. The database represents an initial attempt to provide a set of labeled face photographs spanning the range of conditions typically encountered by people in their everyday lives. This paper describes a face detection system which goes beyond traditional face detection approaches normally designed for single faced images. The system described in this paper has been designed taking into account spatial coherence contained in multiple face detection. The resulting system builds a feature based model for each detected face, and searches them using various model information in the database. It provides a feasible way to locate the positions of two eyeballs, near and far corners of eyes, midpoint of nostrils and mouth corners from face image. This approach would help to extract useful features on human face automatically and improve the accuracy of face recognition.

References

[1]. R. Brunelli and T. Poggio, (1993). “Face Recognition:Features Versus Templates”, IEEE Transaction on PAMI, Vol. 15, No. 10, pp. 1042-1052.

[2]. S. Y. Lee, Y. K. Ham and R. H. Park, (1996). “Recognition of Human Front Faces using Knowledge-Based Feature Extraction and NeuroFuzzy Algorithm”, Pattern Recognition, Vol. 29, No.11, pp. 1863-1876.

[3]. R. S. Feris, T. E. de Campos and R.M. Cesar Junior, (2000). “Detection and tracking of facial features in video sequences”, Lecture Notes in Artificial Intelligence, Vol.1793, No. 4, pp. 127-135.

[4]. G. Chow and X. Li, (1993). “Towards A System for Automatic Facial Feature Detection”, Pattern Recognition, Vol. 26, No. 12, pp. 1739-1755.

[5]. M. Gargesha and S. Panchanathan, (2002). “A Hybrid Technique for Facial Feature Point Detection”, IEEE Proceedings of SSIAI' 2002, pp. 134-138.

[6]. R. S. Feris, J. Gemmell, K. Toyama and V. Kruger, (2002). “Hierarchical Wavelet Networks for Facial Feature Localization”, Proceedings of the Fifth IEEE International Conference on AutomaticFace and Gesture Recognition (FGR.02) , pp. 118-123.

[7]. E. Hjelmas, B.K. (2001). “Low, Face Detection: A Survey”, Computer Vision and Image Understanding, Vol.83, No. 3, pp. 236–274.

[8]. M.-H. Yang, D. Kriegman, N. Ahuja, (2002). “Detecting Faces in Images: A Survey”, Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34–58.

[9]. P. Viola, M.J. Jones (2002). “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol. 57, No. 2, pp. 151–173.

[10]. H. Schneiderman, T. Kanade, (2000). “A Statistical Method for 3D Object Detection Applied to Faces and Cars”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1759.

[11]. P. Viola, M.J. Jones, (2004). “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol. 57, No. 2, pp. 151–173.

[12]. R. Lienhart, A. Kuranov, V. Pisarevsky, (2003). “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection”, DAGM'03, Magdeburg, Germany, pp. 297–304.

[13]. Intel, Intel Open Source Computer Vision Library, b4.0, (August 2004)

[14]. C. Yan and G. D. Su, (1998). “Facial Feature Location and Extraction From Front-View Images”, Journal of Image and Graphics of China, Vol. 3, No. 5, pp. 375-380.

[15]. M. Brown and D.G. Lowe, (2002). “Invariant Features From Interest Pointgroups,” British Machine Vision Conference, pp. 656-665.

[16]. D.G. Lowe, (2004). “Distinctive Image Features From Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, pp. 91-110.

[17]. T. Lindeber, (1998). “Feature Detection with Automatic Scale Selection,” International Journal of Computer Vision, Vol. 30, No.2, pp. 79-116.

Full Article (HTML)

Pdf

Research Paper

An Intelligent System Approach for Handwritten Kannada Word Recognition

M.S. Patel* , S.C. Linga Reddy**

* Associate Professor, Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India.

** Professor & HOD, Department of Computer Science and Engineering, Alpha College of Engineering, Bengaluru, India.

Patel, M. S., and Reddy, S. L. (2015). An Intelligent Approach for Handwritten Kannada Word Recognition. i-manager’s Journal on Pattern Recognition, 2(1),10-15. https://doi.org/10.26634/jpr.2.1.3371

Abstract

The challenges in OCR system are many in the area of Handwritten Character and Word Recognition. From its very nature, Handwritten Character Word is a mixture of cursive and non-cursive segments. This leads to the problem of Recognition being significantly difficult. In this paper, the authors propose an intelligent system for recognition of handwritten Kannada words for recognition of the names of Districts and Taluks written on boards, land marks, etc. The proposed system applies an idea of subspace approach with popular Neural Network Architectures such as Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) for classification. This method is experimented on handwritten words comprising 189 (District & Taluk names of Karnataka State) classes.

References

[1]. Adria Gimenez, Jesus Andres-Ferrer, Alfons Juan, (2014). “Discriminative Bernoulli HMMs for Isolated Handwritten Word Recognition”. Journal Pattern Recognition Letters, Vol.35, pp.157–168.

[2]. A. El-Yacoubi, M. Gilloux, (1999). “An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 8, pp.752-760.

[3]. Brijmohan Singh, Ankush Mittal, M.A. Ansari, Debashis Ghosh, (2011). “Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach”, International Journal on Computer Science and Engineering, Vol. 3, No. 4, pp.1658-1665.

[4]. Ankush Acharyya, Sandip Rakshit, Ram Sarkar, Subhadip Basu, Mita Nasipuri, (2013). “Handwritten Word Recognition using MLP Based Classifier: A Holistic Approach”, IJCSI International Journal of Computer Science Issues, Vol.10, No.2, pp. 422-427.

[5]. Mahdi Hamdani, Patrick Doetsch, Hermann Ney, (2014). “Improvement of Context Dependent Modeling for Arabic Handwriting Recognition”, International Conference on Frontiers in Handwriting Recognition, pp. 494-499.

[6]. Saeed Mozaffari, Karim Faez, Volker Margner, Haikal El-Abed, (2008). “Lexicon Reduction using Dots for Off-line Farsi/Arabic Handwritten Word Recognition”, Journal Pattern Recognition Letters, Vol. 29, No. 6, pp. 724–734.

[7]. M. Blumenstein, C. K. Cheng and X. Y. Liu, (2002). “New preprocessing Techniques for Handwritten Word Recognition”, Proceedings of the Second IASTED.

[8]. Yaregal Assabie, Josef Bigun, (2011). “Offline Handwritten Amharic Word Recognition”, Pattern Recognition Letters, Vol.32, pp.1089–1099.

[9]. Ahlam Maqqor, Akram Halli, and Khaled Satori, (2013). “A Multi-Stream HMM Approach to Offline Handwritten Arabic Word Recognition”, International Journal on Natural Language Computing (IJNLC), Vol. 2, No.4, pp.21-33.

[10]. N. Azizi, N. Farah, M. Sellami, (2005). “Off-line Handwritten Word Recognition using Ensemble of Classifier Selection and Features Fusion”, Vol.14, No.2, pp.141-150.

[11]. Dipak V. Koshti, Sharvari Govilkar, (2012). “Segmentation of Touching Characters in Handwritten Devanagari Script”, UACEE International Journal of Computer Science and its Applications, Vol.2, No.2, pp. 83-87.

[12]. Rajiv Kumar and Amardeep Singh, (2011). “Character Segmentation in Gurumukhi Handwritten Text using Hybrid Approach”, International Journal of Computer Theory and Engineering, Vol. 3, No. 4, pp. 499- 501.

[13]. Vikas J Dongre , Vijay H Mankar, (2011). “Devnagari Document Segmentation using Histogram Approach”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.3, pp. 46- 53.

[14]. Shuchi Kapoor and Vivek Verma, (2014). “Fragmentation of Handwritten Touching Characters in Devanagari Script”, International Journal of Information Technology, Modeling and Computing (IJITMC), Vol. 2, No. 1, pp. 11-21.

[15]. G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis, (2008). “A Complete Optical Character Recognition Methodology for Historical Documents”, IAPR Workshop on Document Analysis Systems, pp. 525 – 532.

[16]. Turk., M and A. Pentland, (1991). “Eigenfaces for Recognition”, Journal of Cognition and Neuroscience, Vol.3, No.1, pp. 71–86.

[17]. Wasserman P D, (1993). Advanced Methods in Neural Computing. pp. 155–161, Van Nostrand Reinhold, New York, USA.

[18]. T. Poggio and F. Girosi, (1990). “Networks for Approximation and Learning”, Proceedings of the IEEE, Vol.78, No.9, pp 1481–1497.

[19]. Donald F Specht, (1990). “Probabilistic Neural Networks”, Journal of Neural Networks, Vol.3, No.1, pp. 109–118.

Full Article (HTML)

Pdf

Research Paper

Comparison of Spatial Domain Features for Writer Recognition Under Different Ink Width Conditions

Sharada Laxman Kore* , Shaila Apte**

* Research Scholar, Bharati Vidyapeeth University College of Engineering, Pune, Maharashtra, India.

** Professor, Department of Electronics and Telecommunication, Rajarshi Shahu college of Engineering, Pune, Maharashtra, India.

Kore, S. L., and Apte, S. (2015). Comparison of Spatial Domain Features for Writer Recognition Under Different Ink Width Conditions. i-manager’s Journal on Pattern Recognition, 2(1), 16-22. https://doi.org/10.26634/jpr.2.1.3372

Abstract

In this paper, the authors tested the usefulness of most commonly used existing methods of writer recognition under different ink width conditions. A comparative study of Spatial Domain Features is presented in this paper. The existing methods give low error rate when they compare two handwritten images with different pen type. To improve the accuracy to a higher level histogram, variance in histogram bins and normalized histogram are used as features to recognize the handwriting. The system is tested for 981 writers with 2 samples, each with different writing instruments. The system is tested using Chain Code and Differential Chain Codes. Experimental result shows that the histogram of chain code outperform the other methods with 90.46 % as the recognition accuracy on this newly created dataset.

References

[1]. Marti U. V. and Bunke H. (2002). “The IAM-Database: An English Sentence Database for Offline Handwriting Recognition”. International Journal on Document Analysis and Recognition, Vol. 5, No. 1, pp. 39-46.

[2]. Kore S. L. and Apte S. D. (2012). “The Current State of Art-Handwriting a Behavioral Biometric for Writer Identification and Verification”. International Conference on Advanced Computing, Communications and Informatics, Aug 3-5,Chennai T. Nadu, ACM Digital library, pp. 978-1-4503-1196-0/12/08.

[3]. Arazi B. (1977). “Handwriting identification by means of run-length measurements”, IEEE Transaction System on Man Cybern, Vol. 7, No.12, pp. 878–881.

[4]. Bulacu M. L. (2007). “Statistical Pattern Recognition for Automatic Writer Identification and Verification”. Ph.D. Thesis.

[5]. Bensefia, T. Paquet, and L. Heutte. (2005). “A Writer Identification and Verification System”. Pattern Recognition Letters, Vol. 26, pp. 2080-2092.

[6]. Akhter N. (2011). “Script Independent Offline Writer Identification Using Handwriting Style”. International Conference on Document Analysis and Recognition, ICDAR 2011.

[7]. Brink A. A., J.Smit b, M.L.Bulacu, L.R.B.Schomaker. (2012). “Writer Identification using Directional Ink-trace Width Measurements”. Pattern Recognition, Vol. 45, pp. 162–171

[8]. Imran Siddiqi and NicoleVincent (2010). “Text Independent Writer Recognition using Redundant Writing Patterns with Contour-based Orientation and Curvature Features”. Pattern Recognition, Vol. 43, pp. 3853–3865.

[9]. Siddiqi I. and Vincent N. (2009). “A Set of Chain th Code based Features for Writer Recognition”. 10 International Conference on Document Analysis and Recognition (ICDAR 09), pp. 981-985, doi. 10. 1109/ICDAR.2009.136.

[10]. Zhimin Huang, Dongli Wang, and Yue Lu (2014). “Writer Identification using Differential Chain Code and Grid Features”, Advances in Information Systems and Computing, Vol. 277, pp. 647-656 , 10.1007/978-3-642- 549243-64, Springer.

[11]. Hong Ding, Huiqun Wu, Xiaofeng Zhang and J. Chen (2014). “Writer Identification based on Local Contour Distribution Feature”, International Journal on Signal Processing, Image Processing and Pattern Recognition, Vol. 7, No. 1, pp. 169-180, doi. 10.14257/ ijsip.2014.7.1.16.

[12]. Soheila S.R. and M.E. Moghaddam (2014). “A Persian Writer Identification Method using Swarm Based Feature Selection Approach”, International Journal on Biometrics, Vol. 6, No. 1, pp. 53-74.

[13]. Siddiqi I. and Vincent N. (2008). “Stroke Width Independent Feature for Writer Identification and Handwriting Classification”, ICFHR.

[14]. Kore S.L. and Apte S .D. (2013). “Ink Width Independent Global Features for Writer Verification”. ICACCI'13, pp. 1770-1775, 978-1-4673-6217-7/13, IEEE.

Full Article (HTML)

Pdf

Research Paper

Language Identification Using MFCC Features Derived During Oration

Manogna Maddali* , Shoba Bindu C**

* P.G Student, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India.

** Associate Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India.

Maddali, M., and Bindu, C. S. (2015). Language Identification Using MFCC Features Derived During Oration. i-manager’s Journal on Pattern Recognition, 2(1), 23-28. https://doi.org/10.26634/jpr.2.1.3373

Abstract

Automatic Language Identification is the task of identifying the Spoken Language, given utterance of speech. Many Communication Systems make use of this LID. Acoustic properties are used in many experiments, as it is easy to differentiate. Instead of using these features, prosodic properties can be used to identify the Language. The main idea is to explore the duration of neighboring syllable like units as a language discriminative feature. This paper proposes a LID which uses the rhythmic properties of Spoken Speech. Prosodic Features are extracted using Mel Frequency Cepstral Coefficients (MFCC). Based on the energy levels in the Signal, Phoneme Recognition is done to identify the syllable, like units. ANN is used to train the system and results are generated. The main focus of this paper is to improve the Recognition Accuracy. The error rate is reduced when compared with other systems.

References

[1]. Jacob L Newman and Stephen J Cox, (2012). “Language Identification Using Visual Features,” IEEE Transaction on Audio, Speech, and Language Processing, Vol. 20, No. 7, pp. 1936–1947.

[2]. Zissman, Marc A., and Kay M. Berkling. “Automatic Language Identification”. Speech Communication, Vol. 35, No. 1, pp. 115-124.

[3]. R. Ramus and J. Mehler (1999). “Language Identification with Suprasegmental cues: A Study Based On Speech Resynthesis”. Journal of Acoustic Society of America, Vol. 5.

[4]. T. Nazzi, J. Bertoncini, and J. Mehler (1998). “Language Discrimination by Newborns: Towards an Understanding of the Role of Rhythm”. In Journal of Experimental Psychology: Human Perception and Performance, Vol. 24, No. 3, pp. 756–766.

[5]. G. Rigoll (2006). “Lecture Script Pattern Recognition”. Lehrstuhl für Mensch–Machine– Communication der Technischen Universität München.

[6]. S. Nakagawa, T. Seino, and Y. Ueda (1994). “Spoken language identification by ergodic HMMs and its state sequences”. Electronics Communication Japan, Pt. 3, Vol. 77, pp. 70–79.

[7]. M. A. Zissman (1996). “Comparison of Four Approaches to Automatic Language Identification of Telephone Speech”. IEEE Transactions on Speech and Audio Processing, Vol. 4, pp. 31–44.

[8]. Thymé-Gobbel, A., and Hutchins, S. E., (1999). “Prosodic Features in Automatic Language Identification Reflect Language Typology”, Proceedings of ICPhS'99, San Francisco.

[9]. Timoshenko, Ekaterina (2012). “Rhythm Information for Automated Spoken Language Identification”. Dissertation, PhD 2012.

[10]. Y. K. Muthusamy, N. Jan, and R. A. Cole (2007). “Perceptual Benchmarks for Automatic Language Identification”. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 333–336, Adelaide, Australia.

[11]. Y. K. Muthusamy, R. A. Cole, and B. Oshika (1992). “The OGI Multi-Language Telephone Speech Corpus”. Proceedings of IEEE International Conference on Spoken Language Processing (ICSLP), pp. 895–898, Banff, Alberta, Canada.

[12]. K. Berkling, T. Arai, R. A. Cole, and E. Barnard, Y. K. Muthusamy (1993). “A Comparison of Approaches to Automatic Language Identification using Telephone Speech”. Proceedings of European Conference on Speech Communication and Technology (Euro speech), pp. 1307–1310, Geneva, Switzerland.

[13]. Farinas, Jérôme, and François Pellegrino (2001). “Automatic rhythm modeli g for language identification”, Proceedings of Eurospeech, Vol. 4, Scandinavia, Aaldorg, pp. 2539-2542.

[14]. F. Ramus, (2002). “Language Discrimination by Newborns: Teasing Apart Phonotactic, Rhythmic, and Intonational Cues,” Annu. Rev. Lang.Acquisit., Vol. 2, No. 1, pp. 85–115.

Full Article (HTML)

Pdf

Review Paper

Mixing Fingerprints : A Survey

Ria Mathews* , Bino Thomas**

* P.G Scholar, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala.

** Assistant Professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala.

Mathews, R., and Thomas, B. (2015). Mixing Fingerprints : A Survey. i-manager’s Journal on Pattern Recognition, 2(1), 29-33. https://doi.org/10.26634/jpr.2.1.3374

Abstract

Biometrics has an important role in privacy protection, when compared to traditional privacy protection methods like tokens, PIN, passwords etc. With the widespread use of fingerprint techniques in authentication systems, privacy protection of the fingerprint becomes an important issue. Traditional encryption is not sufficient for fingerprint privacy protection because decryption is required before the fingerprint matching, which exposes the fingerprint to the attacker. In recent years, significant efforts have been put into developing specific protection techniques for fingerprint. The main objective of this paper is to make a study on mixing fingerprints and how they are used in privacy protection of fingerprints. In this paper various methods of biometric template protection of fingerprint by mixing the fingerprints or multi-biometrics has been surveyed.

References

[1]. Samir Nanavati, Michael Thieme, Raj Nanavati, (2002). “Biometrics: Identity Verification in a Networked World”, John wiley & sons Publishers, ISBN-13: 978-0-471- 09945-1.

[2]. Champod, Christophe, Chris J. Lennard, Pierre Margot, and Milutin Stoilovic (2004). Fingerprints and other Ridge Skin Impressions, CRC press.

[3]. Ross, Arun A., Karthik Nandakumar, and Anil K. Jain (2006). Handbook of Multibiometrics, Vol. 6, Springer Science & Business Media.

[4]. Berrin Yanikoglu and Alisher Kholmatov Sabanci University, (2004). “Combining Multiple Biometrics to Protect Privacy”, Proceedings of ICPR- BCTP Workshop, Cambridge, U.K.

[5]. E. Camlikaya, A. Kholmatov, and B. Yanikoglu, (2008). “Multi-biometric Templates using Fingerprint and Voice,” Proceedings of SPIE, Vol. 69440I, pp. 1-9.

[6]. A. Ross and A. Othman, (2011). “Mixing Fingerprints for th Template Security and Privacy,” Proceedings of 19 European Signal processing Conference (EUSIPCO), Barcelona, Spain.

[7]. A. Othman and A. Ross, (2011). “Mixing Fingerprints for Generating Virtual Identities,” Proceedings of IEEE International Workshop on Information Forensics and Security (WIFS), Foz do Iguacu, Brazil.

[8]. S. Li and A. C. Kot, (2011). “A Novel System for th Fingerprint Privacy Protection,” Proceedings of 7 International Conference of Information Assurance and Security (IAS), pp. 262–266.

[9]. S. Li, A. C. Kot, (2013). “Fingerprint Combination for Privacy Protection”, IEEE Transactions on Information Forensics and security, Vol. 8, No. 2, pp. 350-360.

[10]. Othman, Asem, and Arun Ross (2013). "On Mixing Fingerprints”. IEEE Transactions on Information Forensics and Security, Vol. 8, No. 1, pp. 260-267.

[11]. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar st (2003). Handbook of Fingerprint Recognition, 1 edition New York, Berlin Heidelberg: Springer-Verlag.

[12]. Dmitry O. Gorodnichy. “Evolution and evaluation of biometric systems”, Publised in CISDA’09 Proceedings of the 2nd IEEE International Conference on Computational Intelligence for Security and Defence Applications, pp. 318-325.

[13]. Simson L. Garfinkel (2005). “Design Principles and Pattern for Computer Systems that are Simultaneously Secure and Usable”. Ph.D. Dissertion. MIT Cambridge,MA, pp.56-66.

[14]. Manju, M., and V. Kavitha (2013). “Survey on Fingerprint Enhancement Techniques”. International Journal of Computer Applications, Vol. 62, No. 4 , pp. 41- 47.

[15]. Alonso-Fernandez, Fernando, Josef Bigun, Julian Fierrez, Hartwig Fronthaler, Klaus Kollreider, and Javier Ortega-Garcia (2009). “Fingerprint Recognition”. Guide to Biometric Reference Systems and Performance Evaluation, pp. 51-88. Springer London.

[16]. D. R. Ashbaugh (1999). Quantitative-Qualitative Friction Ridge Analysis. Boca Raton, FL: CRC Press.

[17]. Jain, Anil K., Arun Ross, and Salil Prabhakar (2004). “An introduction to Biometric Recognition”. IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, No. 1, pp. 4-20.

[18]. Alonso-Fernandez, Fernando, Josef Bigun, Julian Fierrez, Hartwig Fronthaler, Klaus Kollreider, and Javier Ortega-Garcia (2009). “Fingerprint Recognition”. Guide to Biometric Reference Systems and Performance Evaluation, pp. 51-88. Springer London.

[19]. Liu, Manhua, Xudong Jiang, and Alex Chichung Kot (2005). "Fingerprint Reference-Point Detection." EURASIP Journal of Advance Signal Processing, No. 4, pp. 498- 509.

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

Most Read

Most Cited

Volume 2 Issue 1 March - May 2015

Face Detection and Recognition Based on Facial Features and Key Points Matching

Basava Raju* , K. Y. Rama Devi**, P. V. Kumar***

Raju, K. B., Ramadevi, Y., and Kumar, P. V. (2015). Face Detection and Recognition Based on Facial Features and Key Points Matching. i-manager’s Journal on Pattern Recognition, 2(1),1-9. https://doi.org/10.26634/jpr.2.1.3370

Abstract

References

Full Article (HTML)

Pdf

An Intelligent System Approach for Handwritten Kannada Word Recognition

M.S. Patel* , S.C. Linga Reddy**

* Associate Professor, Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India. ** Professor & HOD, Department of Computer Science and Engineering, Alpha College of Engineering, Bengaluru, India.

Patel, M. S., and Reddy, S. L. (2015). An Intelligent Approach for Handwritten Kannada Word Recognition. i-manager’s Journal on Pattern Recognition, 2(1),10-15. https://doi.org/10.26634/jpr.2.1.3371

Abstract

References

Full Article (HTML)

Pdf

Comparison of Spatial Domain Features for Writer Recognition Under Different Ink Width Conditions

Sharada Laxman Kore* , Shaila Apte**

* Research Scholar, Bharati Vidyapeeth University College of Engineering, Pune, Maharashtra, India. ** Professor, Department of Electronics and Telecommunication, Rajarshi Shahu college of Engineering, Pune, Maharashtra, India.

Kore, S. L., and Apte, S. (2015). Comparison of Spatial Domain Features for Writer Recognition Under Different Ink Width Conditions. i-manager’s Journal on Pattern Recognition, 2(1), 16-22. https://doi.org/10.26634/jpr.2.1.3372

Abstract

References

Full Article (HTML)

Pdf

Language Identification Using MFCC Features Derived During Oration

Manogna Maddali* , Shoba Bindu C**

* P.G Student, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India. ** Associate Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India.

Maddali, M., and Bindu, C. S. (2015). Language Identification Using MFCC Features Derived During Oration. i-manager’s Journal on Pattern Recognition, 2(1), 23-28. https://doi.org/10.26634/jpr.2.1.3373

Abstract

References

Full Article (HTML)

Pdf

Mixing Fingerprints : A Survey

Ria Mathews* , Bino Thomas**

* P.G Scholar, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala. ** Assistant Professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala.

Mathews, R., and Thomas, B. (2015). Mixing Fingerprints : A Survey. i-manager’s Journal on Pattern Recognition, 2(1), 29-33. https://doi.org/10.26634/jpr.2.1.3374

Abstract

References

Full Article (HTML)

Pdf

Basava Raju* , K. Y. Rama Devi, P. V. Kumar*

* Associate Professor, Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India.

** Professor & HOD, Department of Computer Science and Engineering, Alpha College of Engineering, Bengaluru, India.

* Research Scholar, Bharati Vidyapeeth University College of Engineering, Pune, Maharashtra, India.

** Professor, Department of Electronics and Telecommunication, Rajarshi Shahu college of Engineering, Pune, Maharashtra, India.

* P.G Student, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India.

** Associate Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapuramu, A.P, India.

* P.G Scholar, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala.

** Assistant Professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala.