priya saha

@lpu.in

Assistant Professor, Computer Science & Engineering
LPU Jalandhar

priya saha


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https://researchid.co/priya.lpu

RESEARCH INTERESTS

Image processing, facial expression analysis, machine learning
25

Scopus Publications

249

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • A novel self-attention guided deep neural network for bruise segmentation using infrared imaging
    Dipak Hrishi Das, Sourav Dey Roy, Surajit Dey, Priya Saha, Mrinal Kanti Bhowmik
    Innovations in Systems and Software Engineering, 2025
  • A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods
    Priya Saha, Asim De, Sourav Dey Roy, Mrinal Kanti Bhowmik
    Multimedia Tools and Applications, 2025
  • FvFc-Net: Forged Video Frame Classification Network
    Santanu Das, Sourav Dey Roy, Priya Saha, Mrinal Kanti Bhowmik
    Lecture Notes in Networks and Systems, 2025
  • Applicability of Heavily Compressed JPEG Tampered Images in Social Media and Supervised Machine Learning towards Detection of Forgery
    Saswata Sarkar, Sourav Dey Roy, Santanu Das, Priya Saha, Mrinal Kanti Bhowmik
    2024 3rd International Conference on Advancement in Electrical and Electronic Engineering Icaeee 2024, 2024
    The evolution and progress of mobile devices and social networks have made it a simple task for inexperienced users to acquire photos and share them on social platforms. Due to the availability of media editing softwares, forgers can efficiently spread rumors thereby creating forged images/ videos (i.e., manipulated/ tampered content) which may create negative sentiments among the people of the country. Thus, the availability of highly sophisticated systems/ techniques for finding tampered traces in images and videos has received significant attention. With the availability of the conventional methods for forgery detection, the detection of the forgery in all the categories of images is not possible especially when the manipulated images are heavily compressed. This paper highlights and analyses the challenges of conventional forensic methods for forgery localization in heavily compressed tampered images. To meet the specific necessities and challenges of forgery localization in images, in this paper we investigated the encoder-decoder based deep learning frameworks (i.e., SegNet and U-Net) for segmentation of the forged regions from the holistic tampered images. Also, we have investigated the perception capabilities of the backbone CNN architectures on the encoder-decoder frameworks for precise segmentation of the forged regions from the images. Experiments have been conducted on publicly available datasets including CASIA-V2 and CMFD datasets and our own designed dataset. Experimental results demonstrate that SegNet+MobileNet on CASIA-V2 dataset, U-Net+AlexNet on CMFD dataset, and SegNet+ResNet-101 have been observed to precisely extract the forged portions from the entire altered images with F1-Score of 0.58, 0.57, and 0.49 respectively.
  • Estimation of Abnormal Cell Growth and MCG-Based Discriminative Feature Analysis of Histopathological Breast Images
    Priya Saha, Puja Das, Niharika Nath, Mrinal Kanti Bhowmik
    International Journal of Intelligent Systems, 2023
    The accurate prediction of cancer from microscopic biopsy images has always been a major challenge for medical practitioners and pathologists who manually observe the shape and structure of the cells from tissues under a microscope. Mathematical modelling of cell proliferation helps to predict tumour sizes and optimizes the treatment procedure. This paper introduces a cell growth estimation function that uncovers the growth behaviour of benign and malignant cells. To analyse the cellular level information from tissue images, we propose a minimized cellular graph (MCG) development method. The method extracts cells and produces different features that are useful in classifying benign and malignant tissues. The method’s graphical features enable a precise and timely exploration of huge amounts of data and can help in making predictions and informed decisions. This paper introduces an algorithm for constructing a minimized cellular graph which reduces the computational complexity. A comparative study is performed based on the state-of-the-art classifiers, SVM, decision tree, random forest, nearest neighbor, LDA, Naive Bayes, and ANN. The experimental data are obtained from the BreakHis dataset, which contains 2480 benign and 5429 malignant histopathological images. The proposed technique achieves a 97.7% classification accuracy which is 7% higher than that of the other graph feature-based classification methods. A comparative study reveals a performance improvement for breast cancer classification compared to the state-of-the-art techniques.
  • A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas
    Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    International Journal of Image and Graphics, 2022
    There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.
  • Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection
    Anu Singha, Priya Saha, Mrinal Kanti Bhowmik
    Communications in Computer and Information Science, 2022
  • AGMC-TU Pap-Smear Cytological Image Dataset: Creation, Annotation, and Analysis towards Early Detection of Cervical Cancer
    Sourav Dey Roy, Priya Saha, Niharika Nath, Abhijit Datta, Mrinal Kanti Bhowmik
    Proceedings 2022 IEEE 10th International Conference on Healthcare Informatics Ichi 2022, 2022
    Automatic cervical cancer screening based on pap-smear images is a highly effective tool where the cells are categorized into normal and abnormal. However, success of most automation tool depends on the accurate extraction of features from the pap-smear images that represent some discriminative characteristics between these two categories of cells. In this paper, we described the designing protocols for creation of a new pap-smear image dataset entitled as AGMC-TU Pap-Smear Cytological Image Dataset. The dataset comprises of 50 normal and 50 abnormal pap-smear images belonging to ethnic and non-ethnic populations of low resource cervical cancer prone regions. Moreover, ground truths of suspicious nucleus regions are annotated in terms of pixel oriented binary masks are also provided with the dataset. Analysis of our dataset includes a conventional (i.e., shape features) and deep feature based study of pap-smear images by dividing them into two major groups: normal and abnormal. Outcome of the analysis clearly differentiates normal and abnormal pap-smear images.
  • A review of deep learning models for traffic flow prediction in Autonomous Vehicles
    Avinash Kumar, Priya Saha
    Proceedings IEEE 2020 2nd International Conference on Advances in Computing Communication Control and Networking Icacccn 2020, 2020
    More and more rapid development has been seen in the last decade in self-driving car technologies, mostly developments in the field of machine learning and deep learning. The goal of the paper is to review the latest state of the art in the area of automated driving by using machine learning technologies. For autonomous vehicle usage the estimation of traffic flows is necessary and they agree to make changes about their relevant artefacts (e.g., turn left or correct, travel straight, shift direction, stop or speed). Work on autonomous vehicles has been seen from the current paper on machine learning methods. Moreover, the non-linear dynamic relationship between spatial and temporal data obtained from the surroundings at the previously described adaptive decision-making periods by vehicles does not extend explicitly to current machine learning models in this context. Throughout this paper, we discussed the learning models for autonomous vehicle traffic flux prediction throughout order to equate such models with their applicability in contemporary intelligent transport systems. In comparison, the paper further addresses problems and possible recommendations for science.
  • A Survey on Image Acquisition Protocols for Non-posed Facial Expression Recognition Systems
    Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    Multimedia Tools and Applications, 2019
    Several research methodologies and human face image databases have been developed based on deliberately produced facial expressions of prototypical emotions. However, real-time and spontaneous facial expression recognition cannot be adequately handled by those existing methods and datasets. To address this problem, research efforts have been made to create spontaneous facial expression image datasets as well as to develop algorithms that can process naturally induced affective behavior. This paper introduces these advances and focuses on a small and specific area of spontaneous facial expression recognition. In this paper, we are concentrating on non-posed image acquisition protocols, which strongly influence the subjects for evoking expressions as natural as possible. We categorize the acquisition protocols into four different parts: image acquisition while playing video games, watching emotional videos, during interviews and from other sources. The taxonomy of facial expression acquisition protocols tells about the typical conditions responsible for producing specific facial expressions in that condition. We also address some important design issues related to spontaneous facial expression recognition systems and list the facial expression databases, which are strictly not acted and non-posed. We also put light on the applications of spontaneously evoked facial expression acquisition and recognition because they have potential medical significance. Moreover, we provide a comprehensive analysis and summary of spontaneous facial expression recognition methods by revealing their pros and cons for future researchers.
  • Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA
    Mrinal Kanti Bhowmik, Priya Saha, Anu Singha, Debotosh Bhattacharjee, Paramartha Dutta
    Expert Systems with Applications, 2019
  • Facial component-based blended facial expressions generation from static neutral face images
    Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    Multimedia Tools and Applications, 2018
  • Classification of IR expressive face images from extracted vascular network
    Pinki Paul, Mousumi Sarkar, Priya Saha, Mrinal Kanti Bhowmik
    2016 IEEE Annual India Conference Indicon 2016, 2017
  • Expressions Recognition of North-East Indian (NEI) Faces
    Priya Saha, Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    Multimedia Tools and Applications, 2016
  • An Approach for Automatic Pain Detection through Facial Expression
    Sourav Dey Roy, Mrinal Kanti Bhowmik, Priya Saha, Anjan Kumar Ghosh
    Procedia Computer Science, 2016
  • Performance evaluation of geometric-based hybrid approach for facial feature localization
    Sourav Dey Roy, Priya Saha, Mrinal Kanti Bhowmik, Debanjana Debnath
    Advances in Intelligent Systems and Computing, 2016
  • Mathematical Representations of Blended Facial Expressions towards Facial Expression Modeling
    Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    Procedia Computer Science, 2016
  • Characterization and recognition of mixed emotional expressions in thermal face image
    Priya Saha, Debotosh Bhattacharjee, Barin K. De, Mita Nasipuri
    Proceedings of SPIE the International Society for Optical Engineering, 2016
  • An approach to detect the Region of Interest of expressive face images
    Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri
    Procedia Computer Science, 2015
  • Facial mole detection: An approach towards face identification
    Usha Rani Gogoi, Mrinal Kanti Bhowmik, Priya Saha, Debotosh Bhattacharjee, Barin Kumar De
    Procedia Computer Science, 2015
  • DeitY-TU face database: Its design, multiple camera capturing, characteristics, and evaluation
    Mrinal Kanti Bhowmik, Kankan Saha, Priya Saha, Debotosh Bhattacharjee
    Optical Engineering, 2014
  • Gradient based fusion of infrared and visual face images using support vector machine for human face identification
    Priya Saha, Mrinal K. Bhowmik, Debotosh Bhattacharjee, Barin K. De, Mita Nasipuri
    Proceedings of SPIE the International Society for Optical Engineering, 2013
  • Creation of North-East Indian face database for human face identification
    Kankan Saha, Priya Saha, Mrinal K. Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri
    Proceedings of SPIE the International Society for Optical Engineering, 2013
  • Decision fusion of multisensor images for human face identification in information security
    Handbook of Research on Computational Intelligence for Engineering Science and Business, 2013
  • Decision fusion of multisensor images for human face identification in information security
    Handbook of Research on Computational Intelligence for Engineering Science and Business, 2012

RECENT SCHOLAR PUBLICATIONS

  • A novel self-attention guided deep neural network for bruise segmentation using infrared imaging
    DH Das, S Dey Roy, S Dey, P Saha, MK Bhowmik
    Innovations in Systems and Software Engineering 21 (4), 1123-1131 , 2025
    2025
    Citations: 2
  • A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods
    P Saha, A De, SD Roy, MK Bhowmik
    Multimedia Tools and Applications 84 (9), 6025-6071 , 2025
    2025
    Citations: 5
  • FvFc-Net: Forged Video Frame
    S Das, SD Roy, P Saha, MK Bhowmik
    Applied Computing for Software and Smart Systems: Proceedings of ACSS 2024, 145 , 2025
    2025
  • Exploring the role of miRNA in diabetic neuropathy: from diagnostics to therapeutics
    P Saha, SS Yarra, V Arruri, U Mohan, A Kumar
    Naunyn-Schmiedeberg's Archives of Pharmacology 398 (2), 1129-1144 , 2025
    2025
    Citations: 3
  • FvFc-Net: Forged Video Frame Classification Network
    S Das, SD Roy, P Saha, MK Bhowmik
    International Symposium on Applied Computing for Software and Smart Systems … , 2024
    2024
  • Applicability of Heavily Compressed JPEG Tampered Images in Social Media and Supervised Machine Learning towards Detection of Forgery
    S Sarkar, SD Roy, S Das, P Saha, MK Bhowmik
    2024 3rd International Conference on Advancement in Electrical and … , 2024
    2024
  • TU-IR apple image dataset: Benchmarking, challenges, and asymmetric characterization for bruise detection in application of automatic harvesting
    DH Das, SD Roy, P Saha, MK Bhowmik
    IEEE Transactions on AgriFood Electronics 2 (1), 105-124 , 2024
    2024
    Citations: 4
  • Estimation of Abnormal Cell Growth and MCG‐Based Discriminative Feature Analysis of Histopathological Breast Images
    P Saha, P Das, N Nath, MK Bhowmik
    International Journal of Intelligent Systems 2023 (1), 6318127 , 2023
    2023
    Citations: 5
  • A thermal blended facial expression analysis and recognition system using deformed thermal facial areas
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    International Journal of Image and Graphics 22 (05), 2250049 , 2022
    2022
    Citations: 8
  • AGMC-TU Pap-Smear Cytological Image Dataset: Creation, Annotation, and Analysis towards Early Detection of Cervical Cancer
    SD Roy, P Saha, N Nath, A Datta, MK Bhowmik
    2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), 42-47 , 2022
    2022
    Citations: 1
  • Multimedia Technology for Security and Surveillance in Degraded Vision
    MK Bhowmik, P Saha
    Multimedia Tools and Applications 81, 35245 , 2022
    2022
  • Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection
    A Singha, P Saha, MK Bhowmik
    International Conference on Computer Vision and Image Processing, 118-129 , 2021
    2021
  • A review of deep learning models for traffic flow prediction in Autonomous Vehicles
    A Kumar, P Saha
    2020 2nd International Conference on Advances in Computing, Communication … , 2020
    2020
    Citations: 4
  • A survey on image acquisition protocols for non-posed facial expression recognition systems
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 78 (16), 23329-23368 , 2019
    2019
    Citations: 5
  • Facial component-based blended facial expressions generation from static neutral face images
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 77 (15), 20177-20206 , 2018
    2018
    Citations: 6
  • Enhancement of Robustness of Face Recognition System through Reduced Gaussianity in Log-ICA
    MK Bhowmik, P Saha, A Singha, D Bhattacharjee, P Dutta
    expert systems with applications , 2018
    2018
    Citations: 28
  • Classification of IR expressive face images from extracted vascular network
    P Paul, M Sarkar, P Saha, MK Bhowmik
    2016 IEEE Annual India Conference (INDICON), 1-5 , 2016
    2016
  • Expressions Recognition of North-East Indian (NEI) Faces
    P Saha, MK Bhowmik, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 75 (24), 16781-16807 , 2016
    2016
    Citations: 5
  • Characterization and recognition of mixed emotional expressions in thermal face image
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Infrared imaging systems: Design, analysis, modeling, and testing xxvii 9820 … , 2016
    2016
    Citations: 5
  • Performance Evaluation of Geometric-Based Hybrid Approach for Facial Feature Localization
    SD Roy, P Saha, MK Bhowmik, D Debnath
    Proceedings of Fifth International Conference on Soft Computing for Problem … , 2016
    2016
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • An approach for automatic pain detection through facial expression
    SD Roy, MK Bhowmik, P Saha, AK Ghosh
    Procedia Computer Science 84, 99-106 , 2016
    2016
    Citations: 100
  • Enhancement of Robustness of Face Recognition System through Reduced Gaussianity in Log-ICA
    MK Bhowmik, P Saha, A Singha, D Bhattacharjee, P Dutta
    expert systems with applications , 2018
    2018
    Citations: 28
  • Feature points extraction of thermal face using harris interest point detection
    MK Bhowmik, S Shil, P Saha
    Procedia technology 10, 724-730 , 2013
    2013
    Citations: 19
  • An approach to detect the region of interest of expressive face images
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Procedia Computer Science 46, 1739-1746 , 2015
    2015
    Citations: 15
  • Facial mole detection: an approach towards face identification
    UR Gogoi, MK Bhowmik, P Saha, D Bhattacharjee, BK De
    Procedia Computer Science 46, 1546-1553 , 2015
    2015
    Citations: 12
  • A thermal blended facial expression analysis and recognition system using deformed thermal facial areas
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    International Journal of Image and Graphics 22 (05), 2250049 , 2022
    2022
    Citations: 8
  • DeitY-TU face database: its design, multiple camera capturing, characteristics, and evaluation
    MK Bhowmik, K Saha, P Saha, D Bhattacharjee
    Optical Engineering 53 (10), 102106-102106 , 2014
    2014
    Citations: 7
  • Facial component-based blended facial expressions generation from static neutral face images
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 77 (15), 20177-20206 , 2018
    2018
    Citations: 6
  • Mathematical representations of blended facial expressions towards facial expression modeling
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Procedia Computer Science 84, 94-98 , 2016
    2016
    Citations: 6
  • A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods
    P Saha, A De, SD Roy, MK Bhowmik
    Multimedia Tools and Applications 84 (9), 6025-6071 , 2025
    2025
    Citations: 5
  • Estimation of Abnormal Cell Growth and MCG‐Based Discriminative Feature Analysis of Histopathological Breast Images
    P Saha, P Das, N Nath, MK Bhowmik
    International Journal of Intelligent Systems 2023 (1), 6318127 , 2023
    2023
    Citations: 5
  • A survey on image acquisition protocols for non-posed facial expression recognition systems
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 78 (16), 23329-23368 , 2019
    2019
    Citations: 5
  • Expressions Recognition of North-East Indian (NEI) Faces
    P Saha, MK Bhowmik, D Bhattacharjee, BK De, M Nasipuri
    Multimedia Tools and Applications 75 (24), 16781-16807 , 2016
    2016
    Citations: 5
  • Characterization and recognition of mixed emotional expressions in thermal face image
    P Saha, D Bhattacharjee, BK De, M Nasipuri
    Infrared imaging systems: Design, analysis, modeling, and testing xxvii 9820 … , 2016
    2016
    Citations: 5
  • TU-IR apple image dataset: Benchmarking, challenges, and asymmetric characterization for bruise detection in application of automatic harvesting
    DH Das, SD Roy, P Saha, MK Bhowmik
    IEEE Transactions on AgriFood Electronics 2 (1), 105-124 , 2024
    2024
    Citations: 4
  • A review of deep learning models for traffic flow prediction in Autonomous Vehicles
    A Kumar, P Saha
    2020 2nd International Conference on Advances in Computing, Communication … , 2020
    2020
    Citations: 4
  • Exploring the role of miRNA in diabetic neuropathy: from diagnostics to therapeutics
    P Saha, SS Yarra, V Arruri, U Mohan, A Kumar
    Naunyn-Schmiedeberg's Archives of Pharmacology 398 (2), 1129-1144 , 2025
    2025
    Citations: 3
  • Creation of North-East Indian face database for human face identification
    K Saha, P Saha, MK Bhowmik, D Bhattacharjee, M Nasipuri
    Sensors, Cameras, and Systems for Industrial and Scientific Applications XIV … , 2013
    2013
    Citations: 3
  • Decision fusion of multisensor images for human face identification in information security
    MK Bhowmik, P Saha, G Majumder, D Bhattacharjee
    Handbook of Research on Computational Intelligence for Engineering, Science … , 2013
    2013
    Citations: 3
  • A novel self-attention guided deep neural network for bruise segmentation using infrared imaging
    DH Das, S Dey Roy, S Dey, P Saha, MK Bhowmik
    Innovations in Systems and Software Engineering 21 (4), 1123-1131 , 2025
    2025
    Citations: 2

Publications

• Mrinal Kanti Bhowmik, Priya Saha, Anu Singh, Debotosh Bhattacharjee, Paramartha Dutta,“Enhancement of Robustness of Face Recognition System through Reduced Gaussianity in Log-ICA”, Expert Systems with Applications, Vol. 116, pp. 96-107, February 2019, Elsevier, DOI: 10.1016/j., Impact factor: 4.29 (SCIE Indexed).
• Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri, “A Survey on Image Acquisition Protocols for Non-posed Facial Expression Recognition Systems”, Multimedia Tools and Applications, Springer, Vol. 78, No. 16, pp. 23329-23368, 2019, Springer, DOI: 10.1007/s11042-019-7596-2, Impact factor: 2.10 (SCIE Indexed).
• Priya Saha, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri, “Facial Component-based Blended Facial Expressions Generation from Static Neutral Face Images”, Multimedia Tools and Applications, Vol. 77, No. 15, pp. 20177-20206, 2018, Springer, DOI: 10.1007/s11042-017-5436-9 , Impact factor: 2.10 (SCIE Indexed)
• Priya Saha, Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Barin Kumar De, Mita Nasipuri, “Expressions Recognition in North-East Indian (NEI) Faces”, Multimedia Tools and Applications, Vol. 75, No. 24, pp. 16781–16807, 2016, Springer, DOI: 10.1007/s11042-015-2945-2, Impact Factor: 2.10 (SCIE Indexed).
• Mrinal Kanti Bhowmik, Kankan Saha, Priya Saha, Debotosh Bhattacharjee, "DeitY-TU face database: its design, multiple camera capturing, characteristics, and evaluation," Optical Engineering, Vol. 53, , pp: 102106-1 to24, 2014, SPIE, DOI: 10.1117/1., Impact Factor: 1.209, (SCI Indexed).