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nA human face is the most influential part of humans that can uniquely identify a person. Using all the facial characteristics as biometric, the LIFO system can be applicable in many different ways. Like in everyday life, the most mandatory task in any organization is attendance marking. Earlier, people used to mark their presence using paperwork but now along with the advancement of technology, this system has also changed and converted into digital form. Talking about this project, face recognition approach has been taken using open CV. In this model, we have integrated a camera that captures an image as an input, an algorithm for detecting a face from an input image, encoding and identifying the face. After that, record will be automatically updated on a CSV file. The system is then trained on the authorized student faces and the dB is created for the trained data. The database for cropped images is then created
along with the associated labels. The features are extracted using Harr Cascade classifier. Not only the attendance system but we can also monitor every behavior of students with the help of their face. Like, analyzing the facial expression, the teachers can conclude if the students understanding their lectures or not. This is done with the help of recently evolved technology i.e. Landmark detector, which is trained with large datasets and exhibits excellent robustness against different angles concerning the camera. It is seen that the precision of eye-opening level are evaluated according to the landmarks. The landmark detector algorithm extracts the scaler quantity eyes-aspect-ratio characterizing the opening of eyes in each frame. Finally, the support vector machine detects the blinking of eyes as a pattern of EAR values and displays it on window. After getting this kind of report, teacher can record the behaviors. Emotion analysis is also going to be part of this project which is very helpful for the teachers. The emotions are sorted into six classes namely anger, joy, surprise, disgust, sadness, and fear from face image datasets.
To carry out the particular operation, a camera in several areas like college/classroom, offices, movie auditorium, and in front of a car will be equipped that can be able to recognize the emotions of people introducing a potent new form of artificial intelligence into education for monitoring children for classroom compliance. Here in this project, there will be no existing data about the previous behavior of the students and works on real-time expression tracking.n

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1. AnalyticsIndiaMag (Nov, 2020). A complete guide on building a face attendance system. [Online] Available from: https://analyticsindiamag.com/a-complete-guide-on-building-a-face-attendancesystem/
2. AnalyticsIndiaMag (Jul, 2020). My first CNN project– Emotion detection using convolutional neural network with TPU. [Online] Available from: https://analyticsindiamag. com/my-first-cnn-project-emotion-detection-using-convolutional-neural-network-with-tpu.
3. Chun-Ting Hsu, Wataru Sato, Sakiko Yoshikawa. Enhanced emotional and motor responses to live versus videotaped dynamic facial expressions. Scientific Reports. 2020; 10: 16825. https://www.nature.com/articles/s41598-020-73826-2.
4. MG Frank. International Encyclopedia of the Social & Behavioral Sciences. Facial Expressions. 2001; 5230–5234. https://www.sciencedirect.com/topics/computer-science/facial-expression.
5. Fangbing Qu, Wen-Jing Yan, et al. You should have seen the look on your face…”: Self-awareness of facial expressions. Front Psychol. 2017; 8: 832. https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00832/full.
6. Jose A. Diego-Mas. The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces. https://journals.sagepub.com/doi/full/10.1177/2041669520961123.
7. Chris Frith. Role of facial expressions in social interactions. Philos Trans R Soc Lond B Biol Sci. 2009; 364(1535): 3453–3458.
8. Pyimagesearch. Adrian Rosebrock (May, 2017). Drowsiness detection with Open CV. https://www.pyimagesearch.com/2017/05/08/drowsiness-detection-opencv/.
9. Grant Zhong (Dec, 2019). Drowsiness detection with machine learning. https://towardsdatascience.com/drowsiness-detection-with-machine-learning-765a16ca208a.
10. Jennifer Malsert, Amaya Palama, Edouard Gentaz. Emotional facial perception development in 7, 9- and 11-year-old children: The emergence of a silent eye-tracked emotional other-race effect. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233008.

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nWe present a review of technologies to determine human age and gender using fingerprints. Broadly two methodologies are reviewed i.e. Ridge based human age and gender determination and Image based human age and gender determination. Ridge based techniques uses ridge information with its variant statistics measures for classification of a human fingerprint into different classes of age groups and male/female distinction. These methods do not involve separate classifiers for classification rather determines different thresholds for different classes for classification. On the other hand, Image based techniques uses image processing concepts both in spatial and frequency domain like image transformation in frequency domain. These methods also need separate classifier like KNN (K-Nearest Neighborhood) classifier, SVM (Support Vector Machine) etc. to determine age and gender of a specific human fingerprint. Considering the two approaches different research papers are reviewed and their accuracy is stated for the difference. In the end advantages and disadvantages are concluded for both methodologies along with the future scope in this field.n

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1. Davide Maltoni, Dario Maio, Anil K. Jain and Salil Prabhakar. “Handbook of Fingerprint Recognition.” ISBN 0-387-95431-7, 2003-Springer-Verlag New York, Inc.
2. Sheetlani, Jitendra, and Rajmohan Pardeshi. “Fingerprint based automatic human gender identification.” Int. J. Comput. Appl 170.7 (2017): 1–4.
3. Bergstrom, Brittni Elizabeth. “Effect of Speaker Age and Dialect on Listener Perceptions of Personality.” (2017).
4. Falohun, A.S., O.D. Fenwa, and F.A. Ajala. “A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis.” International Journal of Computer Applications 136.4 (2016): 0975–8887.
5. Abdullah, S.F., et al. “Development of a Fingerprint Gender Classification Algorithm Using Fingerprint Global Features.” International Journal of Advanced Computer Science and Applications (IJACSA) 7.6 (2016).
6. Ritika Dadhwal and Ajmer Singh. “Comparison Between Feature Extraction Techniques For Fingerprint Based Gender Classification Using KNN Classifier. Int.J.Computer Technology & Applications (IJCTA), 9 (11) 2016, pp. 5419–5426.
7. Champod, Christophe, et al. Fingerprints and other ridge skin impressions. CRC press, 2017.
8. Bawolek, Edward John, and Douglas E. Loy. “Method and apparatus for capture of a fingerprint using an electro-optical material.” U.S. Patent Application No. 15/821,942.
9. Bawolek, Edward John, and Douglas E. Loy. “Method for recording a fingerprint image.” U.S. Patent Application No. 16/535,209.
10. Alok Chauhan, Akhil Anjikar, Suchita Tarare. “Study of Ridge Based and Image Based Approach for Fingerprint Gender Classification.” International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) Vol. 3, Issue 3, March 2015.
11. Doroz, Rafal, Krzysztof Wrobel, and Piotr Porwik. “An accurate fingerprint reference point determination method based on curvature estimation of separated ridges.” International Journal of Applied Mathematics and Computer Science 28.1 (2018): 209–225.
12. Thirumoorthi, C. “Fingerprint Based Authentication Using Image Processing Techniques.” (2018).
13. Suwarno, Sri, and P. Insao Santosa. “Short Review of Gender Classification based on Fingerprint using Wavelet Transform.” IJACSA 8.11 (2017).
14. Patil, Abhijit, R. Kruthi, and Shivanand Gornale. “Analysis of multi-modal biometrics system for gender classification using face, iris and fingerprint images.” International Journal of Image, Graphics and Signal Processing 11.5 (2019): 34.
15. Wedpathak, Mr GS, et al. “Fingerprint Based Gender Classification Using ANN.” International Journal of Recent Trends in Engineering & Research 4.3 (2018): 72–75.
16. Champod, Christophe, et al. Fingerprints and other ridge skin impressions. CRC press, 2017.
17. Shaik, Subhani, and Anto A. Micheal. “Automatic age and gender recognition in human face image dataset using convolutional neural network system.” International Journal of Advance Research in Computer Science and Management Studies 4.2 (2016).
18. Kumar, Sandeep, Sukhwinder Singh, and Jagdish Kumar. “A study on face recognition techniques with age and gender classification.” 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017.
19. Azam, Samiul. Visual Aesthetics for Person Identification and Gender Recognition. MS thesis. Graduate Studies, 2017.
20. Gier, Vicki S., and David S. Kreiner. “Recognizing a missing senior citizen in relation to experience with the elderly, demographic characteristics, and personality variables.” Current Psychology (2019): 1–16.
21. Gnanasivam, P., and Dr S. Muttan. “Fingerprint gender classification using wavelet transform and singular value decomposition.” arXiv preprint arXiv:1205.6745 (2012).
22. Gnanasivam, P., “Gender Classification and Age Estimation Using Fingerprint and Ear Features.” Doctor of Philosophy, August 2014, Faculty of Information and Communication Engineering, Anna University, Chennai.
23. Gnanasivam, P., and Dr S. Muttan. “Estimation of age through fingerprints using wavelet transform and singular value decomposition.” International Journal of Biometrics and Bioinformatics (IJBB) 6.2 (2012): 58–67.
24. T. Arulkumaran, Dr. P.E. Sankara Narayan anand, Dr. G. Sundari. “Fingerprint Based Age Estimation Using 2D Discrete Wavelet Transforms and Principal Component Analysis.” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE) Vol. 2, Issue 3, March 2013.

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nSpam is also known as unsolicited commercial email (UCE) has become a major worry for the internet’s and worldwide commerce’s long-term viability. Fake emails and other forgeries, such as phishing, are examples of spam emails. Which aim to collect confidential personal information about users on the network or to act illegally against authority. Spam produces a variety of issues, which can result in financial losses. Spam creates bottlenecks and traffic congestion, limiting memory space, processing power, and speed. Therefore, spam can be classified as one of the most common problems faced by an internet user. Many techniques have been developed to overcome spam. Several spam detection techniques are discussed in this document; so, a solution has been proposed to avoid this problem. This document aims to analyze existing research work on spam detection strategies and approaches, the state of the art, the phenomenon of spam detection, explore the basics of spam detection, the proposed detection scheme and possible mitigation schemes.n

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1. Isobel Heyman, Holan Liang, et al. COVID-19 related increase in childhood tics and tic-like attacks. Arch Dis Child, 2021;106(5):420-421
2. Bruno Gomes, Caio Souza Lima, et al. High Number of Species of Social Wasps (Hymenoptera, Vespidae, Polistinae) Corroborates the Great Biodiversity of Western Amazon: a Survey from Rondônia, Brazil. Sociobiology 2020;67(1):112-120
3. P. Oscar Boykin and Vwani Roychowdhury. Leveraging social networks to fight spam. Computer. 2005;38(4):61-68 4. Dennis Fetterly, et al. Detecting Spam Web Pages through Content Analysis. International World Wide Web Conference Committee (IW3C2). Distribution of these papers is limited to classroom use, and personal use by others. 2006
5. Gilad Mishne. Experiments with mood classification in Blog posts. 2005
6. T. R. Lynam, C. L. A. Clarke, G. V. Cormack. Information Extraction with Term Frequencies. Proceedings of the First International Conference on Human Language Technology Research. 2001
7. Yi Zhou, Huanhuan Li, et al. A Fuzzy-Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection. Emerging Technologies for Vehicular Communication Networks. Wireless Communications and Mobile Computing. 2018;7637059
8. Segal Zindel V, Teasdale John D, et al. The mindfulness-based cognitive therapy adherence scale: inter-rater reliability, adherence to protocol and treatment distinctiveness. Clin Psychol Psychother. 2002; 9(2): 131–138.

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