Face Detection and Recognition Using Raspberry Pi

Dolas, P. R.; Ghogare, Pratiksha; Kshirsagar, Apurva; Khadke, Vidya; Bokefode, Sanjana

doi:10.1007/978-3-030-69921-5_35

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on the construction of face detection networks with low computational cost and sufficient capacity, this efficient method uses convolution factorization to build the network with scattered connections. The EagleEye runs on the embedded device based on the ARM Cortex-A53 (Raspberry Pi1 3b+ [26][27][28][29]) at 21 FPS with the input of VGA resolution with better functional accuracy than methods with the same order of computational complexity.…”

Section: Multimedia Multiprocessor Embedded Architecturementioning

confidence: 99%

Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain

et al. 2021

View full text Add to dashboard Cite

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).

show abstract

Section: Multimedia Multiprocessor Embedded Architecturementioning

confidence: 99%

Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Pi Camera digunakan untuk mengakses brankas dan mengambil citra muka yang nanti akan digunakan untuk melakukan hasil perbandingan dengan muka yang ada di dataset [6].…”

Section: Perancangan Alatunclassified

Sistem Brankas Menggunakan Pengenalan Wajah Berbasis Raspberry PiI

Prasetyo

Gozali

Djuana

et al. 2021

J. Iim. Tek. Elektro

View full text Add to dashboard Cite

The human face can be used for face recognition in order to increase the level of security of a safe deposit box because every person has his/her facial characteristics that have similarities with one another. One of the tasks for face recognition is to compare the face in real-time to the ones in the dataset so that the owner can be verified. This final project aims to implement face recognition based using Raspberry Pi to increase the level of security of the safe deposit box system design. This study uses the Raspberry Pi 3B+ because it has sufficient processing capabilities and has a few pre-built modules that make researching this less difficult. Raspberry Pi uses Linux as the operating system, which has access to a large number of libraries and applications compatible with it [1]. Of the many methods used for face detection, in this final project the Viola-Jones method is being used. From the result of this research, the success rate that was obtained is 60%. This number was obtained after 40 trials, the system was able to detect as much as 24 times [2]. The final results shows that the light intensity greatly affects the performance of the system. The light intensity of 8 Lux has an accuracy rate of 30%, while the 40 Lux has an accuracy rate of 90%.Wajah manusia dapat digunakan dalam pengenalan wajah untuk meningkatkan keamanan brankas karena setiap manusia memiliki fitur-fitur wajah yang berbeda-beda. Salah satu tugas dari pengenalan wajah adalah membandingkan wajah pada citra foto dengan wajah yang telah disimpan di dataset, agar identitas pemilik wajah dapat diketahui. Makalah ini mengimplementasikan pengenalan wajah berbasis Raspberry Pi pada sistem brankas untuk meningkatkan keamanan brankas. Penelitian ini menggunakan Raspberry Pi dikarenakan memiliki kemampuan pemrosesan yang cukup dan memiliki modul yang mempermudah implementasi. Raspberry Pi menggunakan Linux sebagai sistem operasi, yang memiliki akses ke sejumlah besar perpustakaan dan aplikasi yang kompatibel. Dari sekian banyak metode yang telah diaplikasikan untuk deteksi wajah, metode yang dipakai untuk penelitian ini adalah metode Viola Jones. Dari hasil penelitian ini, diperoleh nilai keberhasilan sebesar 60%. Nilai ini diperoleh setelah melakukan percobaan sebanyak 40 kali, dengan keberhasilan deteksi oleh sistem sebanyak 24 kali. Hasil akhir menunjukkan bahwa intensitas cahaya sangat mempengaruhi peforma dari sistem. Ketika intensitas cahaya bernilai 8 Lux didapatkan tingkat akurasi sebesar 30 %, sedangkan ketika intensitas cahaya bernilai 40 Lux maka didapatkan tingkat akurasi sebesar 90%.

show abstract