Authors : Kartik Dogra; Nikhil Kumar; Navneet Kumar Saini; Kamaljeet Kaur; Asmit Kumar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/mrydk95h

DOI : https://doi.org/10.38124/ijisrt/26apr1642

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Conventional biometric attendance systems have been cold transactional, as they maximize administrative efficiency but they pay no attention to the psychological condition of the user. This paper proposes a philosophic fully offline, privacy protecting system called the Emotion-Adaptive Facial Intelligence System (EAFIS), which humanizes the workplace entry. EAFIS uses FaceNet recognition with a Scale-Invariant Geometric Emotion Engine which uses Inter-Pupillary Distance (IPD) normalization to make certain accuracy even when the user is far away in front of the camera. The system includes 60 frame neutral baseline calibration per session to include individual anatomical variation thus vastly decreasing the flicker that is inherent in general models of emotion. After a successful logging, a two-page interface with attendance analytics is shown, then switching to a personalized, non-repeating motivational quote based on the mood that the user is detected to be feeling. The empirical findings indicate that EAFIS has a 100 percent data locality and sub-100ms inference latency, having managed to turn a typical biometric task into a supportive experience that promotes employee interaction and well-being.

Keywords : Affective Computing, Biometrics, Facial Recognition, Edge AI, Privacy-Preserving Systems, Inter-Pupillary Distance (IPD) Normalization, MediaPipe.

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