Syeda Laiba Urooj
FAST
· 2021
·
i17 - 0219
Email
—
Phone
—
LinkedIn
—
GitHub
—
Academic
Program
—
CGPA
—
Year
2021
Education
—
Address
—
DOB
—
Career
Current role
—
Target role
—
Skills
Keras, sklearn, Python, Deep Learning, Google Colab, MTCNN, DCNN, CNN
Verbatim text
The exact text the LLM saw on the page (or the booklet text from the old import).
This is what powers semantic search.
True Detective True Detective is a system for disguised face recognition in CCTV footage with respect to bare face, glasses, hats, fake beard and face masks as disguises. It is a deep learning based system which makes use of keras library and dcnn models. The data include 11 individuals [male, female and children] with 250 pictures each. The system consists of four modules. In module-1, it uses MTCNN for face detection. It divides the faces into aligned and un-aligned and makes two separate face databases. In module-2-3, it passes each database to a dcnn and extract the features from it. The extracted features are combined and then passed to a CNN. In module-4, the cnn then trains on these extracted features and give results on test data. Features include: - Realtime Face Recognition on the following: Bare Face Glasses Hats Fake Beard Face Mask Technology Used: Keras, sklearn, Python, Deep Learning Google Colab Supervisor Name: Ms. Noor Ul Ain Group Members: Syeda Laiba Urooj (i17 - 0219)
AI enrichment
Syeda Laiba Urooj developed a deep learning-based system for disguised face recognition in CCTV footage using Keras and DCNN models. The project successfully handles recognition across various disguises including glasses, hats, and face masks by processing aligned and un-aligned face databases.
Skills (AI)
["Python", "Deep Learning", "Keras", "DCNN", "MTCNN", "Face Recognition", "sklearn", "Google Colab"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2021 (1st Final) (1).pdfFrom job #24 page 240
Created: 1778170890