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Face recognition for real time video using LBPH returns "Timeout when trying to acquire hash specific lock for nodemap creation"

I was trying to implement Face recognition using LBPH recognizer, the code returns segmentation error while compiling at the point of "model->perdict()"

I tried to analyze the error using 'strace', which gave the error as

write(2, "An exception occurred.", 22An exception occurred.) = 22 write(2, "\n", 1 ) = 1 write(2, "Timeout when trying to acquire h"..., 132Timeout when trying to acquire hash specific lock for nodemap creation. Lock name = 'GenICam_XML_0xccc16d9f7e184356e18692846d94316c') = 132 write(2, "\n", 1 ) = 1 write(2, "\n", 1 ) = 1 write(2, "Press Enter to exit.", 20Press Enter to exit.) = 20 write(2, "\n", 1 ) = 1 fstat(0, {st_mode=S_IFCHR|0620, st_rdev=makedev(136, 0), ...}) = 0 mmap(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7fadd2790000 read(0, ^CProcess 3427 detached

Can someone help in resolving the issue

Face recognition for real time video using LBPH returns "Timeout when trying to acquire hash specific lock for nodemap creation"

I was trying to implement Face recognition using LBPH recognizer, the code returns segmentation error while compiling at the point of "model->perdict()"

I tried to analyze the error using 'strace', which gave the error as

write(2, "An exception occurred.", 22An exception occurred.) = 22 write(2, "\n", 1 ) = 1 write(2, "Timeout when trying to acquire h"..., 132Timeout when trying to acquire hash specific lock for nodemap creation. Lock name = 'GenICam_XML_0xccc16d9f7e184356e18692846d94316c') = 132 write(2, "\n", 1 ) = 1 write(2, "\n", 1 ) = 1 write(2, "Press Enter to exit.", 20Press Enter to exit.) = 20 write(2, "\n", 1 ) = 1 fstat(0, {st_mode=S_IFCHR|0620, st_rdev=makedev(136, 0), ...}) = 0 mmap(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7fadd2790000 read(0, ^CProcess 3427 detached

Can someone help in resolving the issue

Im doing real time face recognition from live video feed. The steps that I follow are as follows 1. Detect the face using "LBP_cascade.detectMultiScale(gray, faces)" 2. The detected faces from each frame(case: multiple faces in single frame) is accessed one by one, and covert each face to 'Rect', then that to Mat 3. The Mat vector is then given to model->predict,. (Note: the image is not resized to the size of training image, before passing it for recognition)